From 18dd5a83d0deec7dc3836c52062db7b2c817445e Mon Sep 17 00:00:00 2001 From: scdp Date: Thu, 7 Nov 2019 13:54:07 +0100 Subject: [PATCH 01/91] inital commit --- README.md | 4 +- pysepm/__init__.py | 5 + pysepm/__pycache__/__init__.cpython-35.pyc | Bin 0 -> 278 bytes .../speechEnhPerfMetrics.cpython-35.pyc | Bin 0 -> 12107 bytes pysepm/speechEnhPerfMetrics.py | 356 ++++++++++++++++++ setup.py | 20 + 6 files changed, 383 insertions(+), 2 deletions(-) create mode 100644 pysepm/__init__.py create mode 100644 pysepm/__pycache__/__init__.cpython-35.pyc create mode 100644 pysepm/__pycache__/speechEnhPerfMetrics.cpython-35.pyc create mode 100644 pysepm/speechEnhPerfMetrics.py create mode 100644 setup.py diff --git a/README.md b/README.md index b899d05..e9bba25 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,2 @@ -# pysepm -Python implementation of performance metrics in Loizou's Speech Enhancement book +# py-sepm +Python implementation of performance metrics in Loizou's Speech Enhancement Book diff --git a/pysepm/__init__.py b/pysepm/__init__.py new file mode 100644 index 0000000..9672c8e --- /dev/null +++ b/pysepm/__init__.py @@ -0,0 +1,5 @@ +__version__ = '0.1' + + +from .speechEnhPerfMetrics import fwSNRseg,SNRseg,llr,wss,composite,pesq,stoi + diff --git a/pysepm/__pycache__/__init__.cpython-35.pyc b/pysepm/__pycache__/__init__.cpython-35.pyc new file mode 100644 index 0000000000000000000000000000000000000000..72ae0d5f03518b77eb4837a1368dc52dadceb4a8 GIT binary patch literal 278 zcmX|*F;2uV6htR+;$1*18pIV)nhj7RL_^66v7Hfek^Ci=?AWpctK}9PgL`law^Uq# zYHbPl(!7~wzUFkXID9|;e0l`%mGpmQN;gU2GNA`VVu8MdE?`QC5{5xAmY#(W;q=BPn0W2W3Yo z#Yu2P-Ecb&_vANsGSvy!mts9h9&b4kA$eBNrQjKbo Ua_M$)er0U`qUkzM4f!``d;ZHqiU0rr literal 0 HcmV?d00001 diff --git a/pysepm/__pycache__/speechEnhPerfMetrics.cpython-35.pyc b/pysepm/__pycache__/speechEnhPerfMetrics.cpython-35.pyc new file mode 100644 index 0000000000000000000000000000000000000000..20ad7db3e3ddd3719185b202c1aebd970dcb1305 GIT binary patch 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noverlap + shape = x.shape[:-1]+((x.shape[-1]-noverlap)//step, nperseg) + strides = x.strides[:-1]+(step*x.strides[-1], x.strides[-1]) + result = np.lib.stride_tricks.as_strided(x, shape=shape, + strides=strides) + if window is not None: + result = window * result + return result + +def SNRseg(clean_speech, processed_speech,fs, frameLen=0.03, overlap=0.75): + eps=np.finfo(np.float64).eps + + winlength = round(frameLen*fs) #window length in samples + skiprate = int(np.floor((1-overlap)*frameLen*fs)) #window skip in samples + MIN_SNR = -10 # minimum SNR in dB + MAX_SNR = 35 # maximum SNR in dB + + hannWin=0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) + clean_speech_framed=extractOverlappedWindows(clean_speech,winlength,winlength-skiprate,hannWin) + processed_speech_framed=extractOverlappedWindows(processed_speech,winlength,winlength-skiprate,hannWin) + + signal_energy = np.power(clean_speech_framed,2).sum(-1) + noise_energy = np.power(clean_speech_framed-processed_speech_framed,2).sum(-1) + + segmental_snr = 10*np.log10(signal_energy/(noise_energy+eps)+eps) + segmental_snr[segmental_snrMAX_SNR]=MAX_SNR + segmental_snr=segmental_snr[:-1] # remove last frame -> not valid + return np.mean(segmental_snr) + +def fwSNRseg(cleanSig, enhancedSig, fs, frameLen=0.03, overlap=0.75): + if cleanSig.shape!=enhancedSig.shape: + raise ValueError('The two signals do not match!') + eps=np.finfo(np.float64).eps + cleanSig=cleanSig.astype(np.float64)+eps + enhancedSig=enhancedSig.astype(np.float64)+eps + winlength = round(frameLen*fs) #window length in samples + skiprate = int(np.floor((1-overlap)*frameLen*fs)) #window skip in samples + max_freq = fs/2 #maximum bandwidth + num_crit = 25# number of critical bands + n_fft = 2**np.ceil(np.log2(2*winlength)) + n_fftby2 = int(n_fft/2) + gamma=0.2 + + cent_freq=np.zeros((num_crit,)) + bandwidth=np.zeros((num_crit,)) + + cent_freq[0] = 50.0000; bandwidth[0] = 70.0000; + cent_freq[1] = 120.000; bandwidth[1] = 70.0000; + cent_freq[2] = 190.000; bandwidth[2] = 70.0000; + cent_freq[3] = 260.000; bandwidth[3] = 70.0000; + cent_freq[4] = 330.000; bandwidth[4] = 70.0000; + cent_freq[5] = 400.000; bandwidth[5] = 70.0000; + cent_freq[6] = 470.000; bandwidth[6] = 70.0000; + cent_freq[7] = 540.000; bandwidth[7] = 77.3724; + cent_freq[8] = 617.372; bandwidth[8] = 86.0056; + cent_freq[9] = 703.378; bandwidth[9] = 95.3398; + cent_freq[10] = 798.717; bandwidth[10] = 105.411; + cent_freq[11] = 904.128; bandwidth[11] = 116.256; + cent_freq[12] = 1020.38; bandwidth[12] = 127.914; + cent_freq[13] = 1148.30; bandwidth[13] = 140.423; + cent_freq[14] = 1288.72; bandwidth[14] = 153.823; + cent_freq[15] = 1442.54; bandwidth[15] = 168.154; + cent_freq[16] = 1610.70; bandwidth[16] = 183.457; + cent_freq[17] = 1794.16; bandwidth[17] = 199.776; + cent_freq[18] = 1993.93; bandwidth[18] = 217.153; + cent_freq[19] = 2211.08; bandwidth[19] = 235.631; + cent_freq[20] = 2446.71; bandwidth[20] = 255.255; + cent_freq[21] = 2701.97; bandwidth[21] = 276.072; + cent_freq[22] = 2978.04; bandwidth[22] = 298.126; + cent_freq[23] = 3276.17; bandwidth[23] = 321.465; + cent_freq[24] = 3597.63; bandwidth[24] = 346.136; + + + W=np.array([0.003,0.003,0.003,0.007,0.010,0.016,0.016,0.017,0.017,0.022,0.027,0.028,0.030,0.032,0.034,0.035,0.037,0.036,0.036,0.033,0.030,0.029,0.027,0.026, + 0.026]) + + bw_min=bandwidth[0] + min_factor = np.exp (-30.0 / (2.0 * 2.303));# % -30 dB point of filter + + all_f0=np.zeros((num_crit,)) + crit_filter=np.zeros((num_crit,int(n_fftby2))) + j = np.arange(0,n_fftby2) + + + for i in range(num_crit): + f0 = (cent_freq[i] / max_freq) * (n_fftby2) + all_f0[i] = np.floor(f0); + bw = (bandwidth[i] / max_freq) * (n_fftby2); + norm_factor = np.log(bw_min) - np.log(bandwidth[i]); + crit_filter[i,:] = np.exp (-11 *(((j - np.floor(f0))/bw)**2) + norm_factor) + crit_filter[i,:] = crit_filter[i,:]*(crit_filter[i,:] > min_factor) + + num_frames = len(cleanSig)/skiprate-(winlength/skiprate)# number of frames + start = 1 # starting sample + #window = 0.5*(1 - cos(2*pi*(1:winlength).T/(winlength+1))); + + + hannWin=0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) + f,t,Zxx=stft(cleanSig[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=True, boundary=None, padded=False) + clean_spec=np.abs(Zxx) + clean_spec=clean_spec[:-1,:] + clean_spec=(clean_spec/clean_spec.sum(0)) + f,t,Zxx=stft(enhancedSig[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=True, boundary=None, padded=False) + enh_spec=np.abs(Zxx) + enh_spec=enh_spec[:-1,:] + enh_spec=(enh_spec/enh_spec.sum(0)) + + clean_energy=(crit_filter.dot(clean_spec)) + processed_energy=(crit_filter.dot(enh_spec)) + error_energy=np.power(clean_energy-processed_energy,2) + error_energy[error_energy35]=35 + + return np.mean(distortion) + + +def lpcoeff(speech_frame, model_order): + # ---------------------------------------------------------- + # (1) Compute Autocorrelation Lags + # ---------------------------------------------------------- + + R=correlate(speech_frame,speech_frame) + R=R[len(speech_frame)-1:len(speech_frame)+model_order] + # ---------------------------------------------------------- + # (2) Levinson-Durbin + # ---------------------------------------------------------- + lpparams=np.ones((model_order+1)) + lpparams[1:]=solve_toeplitz(R[0:-1],-R[1:]) + + + return(lpparams,R) + +def llr(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): + eps=np.finfo(np.float64).eps + alpha = 0.95 + winlength = round(frameLen*fs) #window length in samples + skiprate = int(np.floor((1-overlap)*frameLen*fs)) #window skip in samples + if fs<10000: + P = 10 # LPC Analysis Order + else: + P = 16 # this could vary depending on sampling frequency. + + hannWin=0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) + clean_speech_framed=extractOverlappedWindows(clean_speech,winlength,winlength-skiprate,hannWin) + processed_speech_framed=extractOverlappedWindows(processed_speech,winlength,winlength-skiprate,hannWin) + numFrames=clean_speech_framed.shape[0] + numerators = np.zeros((numFrames-1,)) + denominators = np.zeros((numFrames-1,)) + + for ii in range(numFrames-1): + A_clean,R_clean=lpcoeff(clean_speech_framed[ii,:],P) + A_proc,R_proc=lpcoeff(processed_speech_framed[ii,:],P) + + numerators[ii]=A_proc.dot(toeplitz(R_clean).dot(A_proc.T)) + denominators[ii]=A_clean.dot(toeplitz(R_clean).dot(A_clean.T)) + + + frac=numerators/denominators + frac[frac<=0]=1000 + distortion = np.log(frac) + #distortion[distortion>2]=2 # this line is not in composite measure matlab implementation of loizou + distortion = np.sort(distortion) + distortion = distortion[:int(round(len(distortion)*alpha))] + return np.mean(distortion) + + +def findLocPeaks(slope,energy): + num_crit = len(energy) + + loc_peaks=np.zeros_like(slope) + + for ii in range(len(slope)): + n=ii + if slope[ii]>0: + while ((n 0)): + n=n+1 + loc_peaks[ii]=energy[n-1] + else: + while ((n>=0) and (slope[n] <= 0)): + n=n-1 + loc_peaks[ii]=energy[n+1] + + return loc_peaks + + + +def wss(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): + + Kmax = 20 # value suggested by Klatt, pg 1280 + Klocmax = 1 # value suggested by Klatt, pg 1280 + alpha = 0.95 + if clean_speech.shape!=processed_speech.shape: + raise ValueError('The two signals do not match!') + eps=np.finfo(np.float64).eps + clean_speech=clean_speech.astype(np.float64)+eps + processed_speech=processed_speech.astype(np.float64)+eps + winlength = round(frameLen*fs) #window length in samples + skiprate = int(np.floor((1-overlap)*frameLen*fs)) #window skip in samples + max_freq = fs/2 #maximum bandwidth + num_crit = 25# number of critical bands + n_fft = 2**np.ceil(np.log2(2*winlength)) + n_fftby2 = int(n_fft/2) + + cent_freq=np.zeros((num_crit,)) + bandwidth=np.zeros((num_crit,)) + + cent_freq[0] = 50.0000; bandwidth[0] = 70.0000; + cent_freq[1] = 120.000; bandwidth[1] = 70.0000; + cent_freq[2] = 190.000; bandwidth[2] = 70.0000; + cent_freq[3] = 260.000; bandwidth[3] = 70.0000; + cent_freq[4] = 330.000; bandwidth[4] = 70.0000; + cent_freq[5] = 400.000; bandwidth[5] = 70.0000; + cent_freq[6] = 470.000; bandwidth[6] = 70.0000; + cent_freq[7] = 540.000; bandwidth[7] = 77.3724; + cent_freq[8] = 617.372; bandwidth[8] = 86.0056; + cent_freq[9] = 703.378; bandwidth[9] = 95.3398; + cent_freq[10] = 798.717; bandwidth[10] = 105.411; + cent_freq[11] = 904.128; bandwidth[11] = 116.256; + cent_freq[12] = 1020.38; bandwidth[12] = 127.914; + cent_freq[13] = 1148.30; bandwidth[13] = 140.423; + cent_freq[14] = 1288.72; bandwidth[14] = 153.823; + cent_freq[15] = 1442.54; bandwidth[15] = 168.154; + cent_freq[16] = 1610.70; bandwidth[16] = 183.457; + cent_freq[17] = 1794.16; bandwidth[17] = 199.776; + cent_freq[18] = 1993.93; bandwidth[18] = 217.153; + cent_freq[19] = 2211.08; bandwidth[19] = 235.631; + cent_freq[20] = 2446.71; bandwidth[20] = 255.255; + cent_freq[21] = 2701.97; bandwidth[21] = 276.072; + cent_freq[22] = 2978.04; bandwidth[22] = 298.126; + cent_freq[23] = 3276.17; bandwidth[23] = 321.465; + cent_freq[24] = 3597.63; bandwidth[24] = 346.136; + + + W=np.array([0.003,0.003,0.003,0.007,0.010,0.016,0.016,0.017,0.017,0.022,0.027,0.028,0.030,0.032,0.034,0.035,0.037,0.036,0.036,0.033,0.030,0.029,0.027,0.026, + 0.026]) + + bw_min=bandwidth[0] + min_factor = np.exp (-30.0 / (2.0 * 2.303));# % -30 dB point of filter + + all_f0=np.zeros((num_crit,)) + crit_filter=np.zeros((num_crit,int(n_fftby2))) + j = np.arange(0,n_fftby2) + + + for i in range(num_crit): + f0 = (cent_freq[i] / max_freq) * (n_fftby2) + all_f0[i] = np.floor(f0); + bw = (bandwidth[i] / max_freq) * (n_fftby2); + norm_factor = np.log(bw_min) - np.log(bandwidth[i]); + crit_filter[i,:] = np.exp (-11 *(((j - np.floor(f0))/bw)**2) + norm_factor) + crit_filter[i,:] = crit_filter[i,:]*(crit_filter[i,:] > min_factor) + + num_frames = len(clean_speech)/skiprate-(winlength/skiprate)# number of frames + start = 1 # starting sample + + hannWin=0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) + scale = np.sqrt(1.0 / hannWin.sum()**2) + + f,t,Zxx=stft(clean_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=True, boundary=None, padded=False) + clean_spec=np.power(np.abs(Zxx)/scale,2) + clean_spec=clean_spec[:-1,:] + + f,t,Zxx=stft(processed_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=True, boundary=None, padded=False) + proc_spec=np.power(np.abs(Zxx)/scale,2) + proc_spec=proc_spec[:-1,:] + + clean_energy=(crit_filter.dot(clean_spec)) + log_clean_energy=10*np.log10(clean_energy) + log_clean_energy[log_clean_energy<-100]=-100 + proc_energy=(crit_filter.dot(proc_spec)) + log_proc_energy=10*np.log10(proc_energy) + log_proc_energy[log_proc_energy<-100]=-100 + + log_clean_energy_slope=np.diff(log_clean_energy,axis=0) + log_proc_energy_slope=np.diff(log_proc_energy,axis=0) + + dBMax_clean = np.max(log_clean_energy,axis=0) + dBMax_processed = np.max(log_proc_energy,axis=0) + + numFrames=log_clean_energy_slope.shape[-1] + + clean_loc_peaks=np.zeros_like(log_clean_energy_slope) + proc_loc_peaks=np.zeros_like(log_proc_energy_slope) + for ii in range(numFrames): + clean_loc_peaks[:,ii]=findLocPeaks(log_clean_energy_slope[:,ii],log_clean_energy[:,ii]) + proc_loc_peaks[:,ii]=findLocPeaks(log_proc_energy_slope[:,ii],log_proc_energy[:,ii]) + + + Wmax_clean = Kmax / (Kmax + dBMax_clean - log_clean_energy[:-1,:]) + Wlocmax_clean = Klocmax / ( Klocmax + clean_loc_peaks - log_clean_energy[:-1,:]) + W_clean = Wmax_clean * Wlocmax_clean + + Wmax_proc = Kmax / (Kmax + dBMax_processed - log_proc_energy[:-1]) + Wlocmax_proc = Klocmax / ( Klocmax + proc_loc_peaks - log_proc_energy[:-1,:]) + W_proc = Wmax_proc * Wlocmax_proc + + W = (W_clean + W_proc)/2.0 + + distortion=np.sum(W*(log_clean_energy_slope- log_proc_energy_slope)**2,axis=0) + distortion=distortion/np.sum(W,axis=0) + distortion = np.sort(distortion) + distortion = distortion[:int(round(len(distortion)*alpha))] + return np.mean(distortion) + +def pesq(clean_speech, processed_speech, fs): + if fs == 8000: + pesq_mos = pypesq(fs,clean_speech, processed_speech, 'nb') + pesq_mos = 46607/14945 - (2000*np.log(1/(pesq_mos/4 - 999/4000) - 1))/2989 #remap to raw pesq score + + elif fs == 16000: + pesq_mos = pypesq(fs,clean_speech, processed_speech, 'wb') + elif fs >= 16000: + numSamples=round(len(clean_speech)/fs*16000) + pesq_mos = pypesq(fs,resample(clean_speech, numSamples), resample(processed_speech, numSamples), 'wb') + else: + numSamples=round(len(clean_speech)/fs*8000) + pesq_mos = pypesq(fs,resample(clean_speech, numSamples), resample(processed_speech, numSamples), 'nb') + pesq_mos = 46607/14945 - (2000*np.log(1/(pesq_mos/4 - 999/4000) - 1))/2989 #remap to raw pesq score + + return pesq_mos + + + +def composite(clean_speech, processed_speech, fs): + wss_dist=wss(clean_speech, processed_speech, fs) + llr_mean=llr(clean_speech, processed_speech, fs) + segSNR=SNRseg(clean_speech, processed_speech, fs) + pesq_mos = pesq(clean_speech, processed_speech,fs) + + Csig = 3.093 - 1.029*llr_mean + 0.603*pesq_mos-0.009*wss_dist + Csig = np.max((1,Csig)) + Csig = np.min((5, Csig)) # limit values to [1, 5] + Cbak = 1.634 + 0.478 *pesq_mos - 0.007*wss_dist + 0.063*segSNR + Cbak = np.max((1, Cbak)) + Cbak = np.min((5,Cbak)) # limit values to [1, 5] + Covl = 1.594 + 0.805*pesq_mos - 0.512*llr_mean - 0.007*wss_dist + Covl = np.max((1, Covl)) + Covl = np.min((5, Covl)) # limit values to [1, 5] + return Csig,Cbak,Covl diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..8c83cea --- /dev/null +++ b/setup.py @@ -0,0 +1,20 @@ +from setuptools import setup +from setuptools import find_packages + +setup( + name='pysepm', + version='0.1', + description='Computes Objective Quality measures', + author='Philipp Schmid', + author_email='scdp@zhaw.ch', + url='https://github.zhaw.ch/scdp/py-sepm', + license='MIT', + install_requires=['numpy', 'scipy','pystoi'], + classifiers=[ + 'Development Status :: 4 - Beta', + 'Intended Audience :: Science/Research', + 'License :: OSI Approved :: MIT License', + 'Programming Language :: Python :: 3' + ], + packages=find_packages() +) From d8c283cc0c97937df9a8d715a74121e68f0bb2f9 Mon Sep 17 00:00:00 2001 From: scdp Date: Thu, 7 Nov 2019 13:55:08 +0100 Subject: [PATCH 02/91] remove cache --- pysepm/__pycache__/__init__.cpython-35.pyc | Bin 278 -> 0 bytes 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zegXW*-rBcgRVYe^=o17fAPYd!KxVSQML^ZTooPkjD!^F=t)dJ02=O||Dg`+67eMHM zcfjdB(|mZbh+fA5g0p|bw~;8ai{d42u9KU4ZfSFfi@3Ss3k@AGIq!%H zmgcCD(9IFRbo zTSY#!2d4XSJ9s+^y~NEZP_@$ns`qeJJ2>L!e!1<@i&xG5gc|KAP_@$nqHiil$`Vbs zZX%a};l}Hy)43jySh+NOm)v(T-PFN4xk9WMa;$AdH5QRK0j^U%ZHl5 zgw@N^n{n$)pZ(X*f9vRLjg!!a z8Qz`v7=;deumo@q9x$}17$aaiB|DHLgRnX&Ivc|QN?xYu-NmFIXJI%qO9ts(4Wq|@nHq@@S*%MV=eUUZ>cvU3&lAl^oBXg^lt3ir+bFKwWD4gdfE From af5185f37119e7a3b6ebd8bd6a2f6f6378723a49 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 14:00:31 +0100 Subject: [PATCH 03/91] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e9bba25..22d2b10 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,2 @@ -# py-sepm +# pysepm Python implementation of performance metrics in Loizou's Speech Enhancement Book From 7d4c9d2936b0fa4fdfd425fa9248dd3e1787394f Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 14:05:38 +0100 Subject: [PATCH 04/91] Update README.md --- README.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/README.md b/README.md index 22d2b10..58d40d9 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,20 @@ # pysepm Python implementation of performance metrics in Loizou's Speech Enhancement Book + +# Requirements + + numpy + scipy + pystoi + pypesq + +# Implemented Quality Measures ++ fwSNRseg ++ SNRseg ++ llr ++ wss ++ pesq ++ composite + +# Implemented Intelligibility Measures ++ stoi From 365b5364ebb1ddff891e1617d29767e645887a02 Mon Sep 17 00:00:00 2001 From: scdp Date: Thu, 7 Nov 2019 15:05:06 +0100 Subject: [PATCH 05/91] example notebook added --- tests/testPerfMetrics.ipynb | 557 ++++++++++++++++++++++++++++++++++++ 1 file changed, 557 insertions(+) create mode 100644 tests/testPerfMetrics.ipynb diff --git a/tests/testPerfMetrics.ipynb b/tests/testPerfMetrics.ipynb new file mode 100644 index 0000000..5db327d --- /dev/null +++ b/tests/testPerfMetrics.ipynb @@ -0,0 +1,557 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.io import wavfile\n", + "from scipy.signal import stft,get_window, correlate\n", + "import numpy as np\n", + "import pysepm as pm #import fwSNRseg,SNRseg,llr,wss, composite,pesq,stoi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# frequency weighted segmental SNR (fwSNRSeg)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3.3554001065824384" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "pm.fwSNRseg(cleanSig, enhancedSig, fs)# should be 3.3554\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10.093763940018947" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.fwSNRseg(cleanSig, enhancedSig, fs)# should be 10.0938\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12.560192001043312" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.fwSNRseg(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 12.5602\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Segmental SNR (SNRSeg)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-4.0386645840708395" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "pm.SNRseg(cleanSig, enhancedSig, fs)# should be -4.0387\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.959472693785315" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.SNRseg(cleanSig, enhancedSig, fs)# should be 0.9595\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.281379775527017" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.SNRseg(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 5.2814\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Log Likelihood Ratio (LLR)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9607521284185142" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "pm.llr(cleanSig, enhancedSig, fs)# should be 0.9593\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6399952859427331" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.llr(cleanSig, enhancedSig, fs)# should be 0.6400\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.22273917406045848" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.llr(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 0.2227" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Weighted Spectral Slope (WSS)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "52.65786610835284" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "pm.wss(cleanSig, enhancedSig, fs)# should be 52.6579" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "37.649017195834844" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.wss(cleanSig, enhancedSig, fs)# should be 37.6490" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19.612652522957717" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.wss(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 19.6127" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PESQ" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0832337141036987" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "pm.pesq(cleanSig, enhancedSig, fs)# should be 1.0832" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.463416557463939" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.pesq(cleanSig, enhancedSig, fs)# should be 2.4634" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.7307331381405935" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.pesq(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 2.7293" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Composite Objective Speech Quality Measure" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.283655194486704, 1.5287447837866348, 1.6054929873447288)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "pm.composite(cleanSig, enhancedSig, fs)# should be (2.2837,1.5287,1.6055)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3.5810438801531697, 2.608416773805394, 2.985829621984948)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.composite(cleanSig, enhancedSig, fs)# should be (3.5810,2.6084,2.9858)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4.333919599483947, 3.1347287982287013, 3.540909151423519)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.composite(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be (4.3330,3.1340,3.5397)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# STOI" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6739177895331303" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "pm.stoi(cleanSig, enhancedSig, fs)# should be 0.6739" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8934582975768947" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.stoi(cleanSig, enhancedSig, fs)# should be 0.8934" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8916166026077093" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "pm.stoi(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 0.8917" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 68af55ae5b671fb36f36ee729c7e93b9c9ac675e Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 15:13:18 +0100 Subject: [PATCH 06/91] Update README.md --- README.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 58d40d9..1bb1d86 100644 --- a/README.md +++ b/README.md @@ -8,13 +8,13 @@ Python implementation of performance metrics in Loizou's Speech Enhancement Book pystoi pypesq -# Implemented Quality Measures -+ fwSNRseg -+ SNRseg -+ llr -+ wss -+ pesq -+ composite +# Implemented Speech Quality Measures ++ Segmental Signal-to-Noise Ratio (SNRseg) ++ Frequency-weighted Segmental SNR (fwSNRseg) ++ Log-likelihood Ratio (llr) ++ Weighted Spectral Slope (wss) ++ Perceptual Evaluation of Speech Quality (pesq) ++ Composite Objective Speech Quality (composite) -# Implemented Intelligibility Measures -+ stoi +# Implemented Speech Intelligibility Measures ++ Short-time objective intelligibility (stoi) From 2e33a0c7f67e41c91a26ed8878a2d3697cfb9952 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 15:14:28 +0100 Subject: [PATCH 07/91] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1bb1d86..81fa0e4 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ -# pysepm -Python implementation of performance metrics in Loizou's Speech Enhancement Book +# pysepm - Speech Enhancement Performance Measures (Quality and Intelligibility) Implemented in Python +Python implementation of performance measures in Loizou's Speech Enhancement Book # Requirements From 516408f0375a983cbc2e8095a97603ad190959e0 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 15:18:44 +0100 Subject: [PATCH 08/91] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 81fa0e4..dab0675 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ # pysepm - Speech Enhancement Performance Measures (Quality and Intelligibility) Implemented in Python -Python implementation of performance measures in Loizou's Speech Enhancement Book +Python implementation of performance measures in Philipos C. Loizou's [Speech Enhancement Book](https://www.crcpress.com/Speech-Enhancement-Theory-and-Practice-Second-Edition/Loizou/p/book/9781138075573) + # Requirements From 58acc248791f7b12b3f027e58a253964f561351a Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 15:21:45 +0100 Subject: [PATCH 09/91] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index dab0675..07f8f65 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # pysepm - Speech Enhancement Performance Measures (Quality and Intelligibility) Implemented in Python -Python implementation of performance measures in Philipos C. Loizou's [Speech Enhancement Book](https://www.crcpress.com/Speech-Enhancement-Theory-and-Practice-Second-Edition/Loizou/p/book/9781138075573) +Python implementation of objective quality and intelligibilty measures mentioned in Philipos C. Loizou's great [Speech Enhancement Book](https://www.crcpress.com/Speech-Enhancement-Theory-and-Practice-Second-Edition/Loizou/p/book/9781138075573). The Python implementations are checked with the MATLAB implementations attached to the book. # Requirements From 810ae98eb81bf51a87b524c85c2dea1a519c05ae Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 17:20:12 +0100 Subject: [PATCH 10/91] install required modules --- setup.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 8c83cea..ca53c04 100644 --- a/setup.py +++ b/setup.py @@ -9,7 +9,11 @@ author_email='scdp@zhaw.ch', url='https://github.zhaw.ch/scdp/py-sepm', license='MIT', - install_requires=['numpy', 'scipy','pystoi'], + install_requires=['numpy', + 'scipy', + 'pystoi @ https://github.com/schmiph2/pystoi/archive/master.zip#egg=pystoi', + 'pypesq @ https://github.com/schmiph2/python-pesq/archive/master.zip#egg=pypesq', + ], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Science/Research', From a084f0108e8f078bfe442cf8bc13d0058f9cf028 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 17:43:51 +0100 Subject: [PATCH 11/91] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index ca53c04..58651e3 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ install_requires=['numpy', 'scipy', 'pystoi @ https://github.com/schmiph2/pystoi/archive/master.zip#egg=pystoi', - 'pypesq @ https://github.com/schmiph2/python-pesq/archive/master.zip#egg=pypesq', + 'pypesq @ https://github.com/schmiph2/python-pesq/archive/master.zip', ], classifiers=[ 'Development Status :: 4 - Beta', From dcf38c93a527f92fffdfc730e95748495ac805c2 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 17:46:22 +0100 Subject: [PATCH 12/91] Update setup.py --- setup.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 58651e3..b87d589 100644 --- a/setup.py +++ b/setup.py @@ -12,8 +12,11 @@ install_requires=['numpy', 'scipy', 'pystoi @ https://github.com/schmiph2/pystoi/archive/master.zip#egg=pystoi', - 'pypesq @ https://github.com/schmiph2/python-pesq/archive/master.zip', - ], + 'pypesq', + ], + dependency_links=[ + 'git://git@github.com/schmiph2/python-pesq.git#egg=pypesq', + ] classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Science/Research', From b85b8407dfd3f2321bdf6051ab4286d589b7a399 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 17:46:42 +0100 Subject: [PATCH 13/91] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index b87d589..69379d5 100644 --- a/setup.py +++ b/setup.py @@ -16,7 +16,7 @@ ], dependency_links=[ 'git://git@github.com/schmiph2/python-pesq.git#egg=pypesq', - ] + ], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Science/Research', From 72954399244ce253ba8fbafa303dd14505d04b99 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 17:47:37 +0100 Subject: [PATCH 14/91] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 69379d5..de7bf27 100644 --- a/setup.py +++ b/setup.py @@ -14,7 +14,7 @@ 'pystoi @ https://github.com/schmiph2/pystoi/archive/master.zip#egg=pystoi', 'pypesq', ], - dependency_links=[ + dependency_links=[ 'git://git@github.com/schmiph2/python-pesq.git#egg=pypesq', ], classifiers=[ From c980a22f4924b23483a4b742e2eeb60a5b6fc2f0 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 17:49:30 +0100 Subject: [PATCH 15/91] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index de7bf27..6f4d132 100644 --- a/setup.py +++ b/setup.py @@ -15,7 +15,7 @@ 'pypesq', ], dependency_links=[ - 'git://git@github.com/schmiph2/python-pesq.git#egg=pypesq', + 'git+https://github.com/schmiph2/python-pesq.git@master#egg=pypesq', ], classifiers=[ 'Development Status :: 4 - Beta', From f222dd08f3cfbfc1eee42fc0ef09027c04d516dd Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 17:52:48 +0100 Subject: [PATCH 16/91] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 6f4d132..9f96280 100644 --- a/setup.py +++ b/setup.py @@ -15,7 +15,7 @@ 'pypesq', ], dependency_links=[ - 'git+https://github.com/schmiph2/python-pesq.git@master#egg=pypesq', + 'git+https://github.com/schmiph2/python-pesq@e73f9b3ab6ce8d27661b6193c9b0a2cecb6d8396#egg=pypesq', ], classifiers=[ 'Development Status :: 4 - Beta', From e9d0a11fa494298766d1728feefd1a378fd88b2c Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 7 Nov 2019 22:07:29 +0100 Subject: [PATCH 17/91] Update setup.py --- setup.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/setup.py b/setup.py index 9f96280..f4a3bb2 100644 --- a/setup.py +++ b/setup.py @@ -11,11 +11,8 @@ license='MIT', install_requires=['numpy', 'scipy', - 'pystoi @ https://github.com/schmiph2/pystoi/archive/master.zip#egg=pystoi', - 'pypesq', - ], - dependency_links=[ - 'git+https://github.com/schmiph2/python-pesq@e73f9b3ab6ce8d27661b6193c9b0a2cecb6d8396#egg=pypesq', + 'pystoi @ https://github.com/schmiph2/pystoi/archive/master.zip#egg=pystoi-1.0.0', + 'pypesq @ https://github.com/schmiph2/python-pesq/archive/master.zip#egg=pypesq-1.0.0', ], classifiers=[ 'Development Status :: 4 - Beta', From 27dfcd7ac2abf3cc3b359a46d900e27f5c1de52a Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Fri, 8 Nov 2019 06:54:22 +0100 Subject: [PATCH 18/91] Update README.md --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 07f8f65..73e0a60 100644 --- a/README.md +++ b/README.md @@ -19,3 +19,8 @@ Python implementation of objective quality and intelligibilty measures mentioned # Implemented Speech Intelligibility Measures + Short-time objective intelligibility (stoi) + +# Install with pip +``` +$ pip3 install https://github.com/schmiph2/pysepm/archive/master.zip +``` From 819b012c71fe16251b3d2eb5d800f370c1170705 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Fri, 8 Nov 2019 07:00:17 +0100 Subject: [PATCH 19/91] Update README.md --- README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/README.md b/README.md index 73e0a60..783db2b 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,14 @@ Python implementation of objective quality and intelligibilty measures mentioned + Short-time objective intelligibility (stoi) # Install with pip +The setup.py is not finished yet. You have to install the dependencies by hand ``` +First install Cython (Required for python-pesq): +$ pip3 install Cython + +Then install python-pesq and pystoi +$ pip3 install https://github.com/schmiph2/python-pesq/archive/master.zip +$ pip3 install https://github.com/schmiph2/pystoi/archive/master.zip +Finally install pysepm: $ pip3 install https://github.com/schmiph2/pysepm/archive/master.zip ``` From 0e0392b9eed41a0b227f89400d34852a0534c6e5 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Fri, 8 Nov 2019 07:01:06 +0100 Subject: [PATCH 20/91] Update README.md --- README.md | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 783db2b..6b14a6b 100644 --- a/README.md +++ b/README.md @@ -22,13 +22,17 @@ Python implementation of objective quality and intelligibilty measures mentioned # Install with pip The setup.py is not finished yet. You have to install the dependencies by hand -``` -First install Cython (Required for python-pesq): -$ pip3 install Cython +First install Cython (Required for python-pesq): +``` +pip3 install Cython +``` Then install python-pesq and pystoi -$ pip3 install https://github.com/schmiph2/python-pesq/archive/master.zip -$ pip3 install https://github.com/schmiph2/pystoi/archive/master.zip +``` +pip3 install https://github.com/schmiph2/python-pesq/archive/master.zip +pip3 install https://github.com/schmiph2/pystoi/archive/master.zip +``` Finally install pysepm: -$ pip3 install https://github.com/schmiph2/pysepm/archive/master.zip +``` +pip3 install https://github.com/schmiph2/pysepm/archive/master.zip ``` From b7502077e947474ba67f48921eebf35d1353be79 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Fri, 8 Nov 2019 07:01:29 +0100 Subject: [PATCH 21/91] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 6b14a6b..2c6261d 100644 --- a/README.md +++ b/README.md @@ -21,13 +21,13 @@ Python implementation of objective quality and intelligibilty measures mentioned + Short-time objective intelligibility (stoi) # Install with pip -The setup.py is not finished yet. You have to install the dependencies by hand +The setup.py is not finished yet. You have to install the dependencies by hand. First install Cython (Required for python-pesq): ``` pip3 install Cython ``` -Then install python-pesq and pystoi +Then install python-pesq and pystoi: ``` pip3 install https://github.com/schmiph2/python-pesq/archive/master.zip pip3 install https://github.com/schmiph2/pystoi/archive/master.zip From 325076c69eae0bc9c26a0f848ea9b459037d85b5 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Fri, 8 Nov 2019 07:04:34 +0100 Subject: [PATCH 22/91] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 2c6261d..000282c 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,8 @@ Python implementation of objective quality and intelligibilty measures mentioned # Requirements + gcc compiler + cython numpy scipy pystoi From 15cd2138fa25077a5f4e27de1abbeca0a3034280 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 19 Nov 2019 13:48:46 +0100 Subject: [PATCH 23/91] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 000282c..8185037 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # pysepm - Speech Enhancement Performance Measures (Quality and Intelligibility) Implemented in Python -Python implementation of objective quality and intelligibilty measures mentioned in Philipos C. Loizou's great [Speech Enhancement Book](https://www.crcpress.com/Speech-Enhancement-Theory-and-Practice-Second-Edition/Loizou/p/book/9781138075573). The Python implementations are checked with the MATLAB implementations attached to the book. +Python implementation of objective quality and intelligibilty measures mentioned in Philipos C. Loizou's great [Speech Enhancement Book](https://www.crcpress.com/Speech-Enhancement-Theory-and-Practice-Second-Edition/Loizou/p/book/9781138075573). The Python implementations are checked with the MATLAB implementations attached to the book (see [Link](https://crcpress.com/downloads/K14513/K14513_CD_Files.zip)) # Requirements From 12f616072e42fc880f24372a1e525a3e8833d7ee Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Mon, 15 Jun 2020 18:54:41 +0200 Subject: [PATCH 24/91] CSII metric added --- pysepm/speechEnhPerfMetrics.py | 157 +++++++++++++++++++++++++++++++++ 1 file changed, 157 insertions(+) diff --git a/pysepm/speechEnhPerfMetrics.py b/pysepm/speechEnhPerfMetrics.py index 2f68d46..e3989c6 100644 --- a/pysepm/speechEnhPerfMetrics.py +++ b/pysepm/speechEnhPerfMetrics.py @@ -354,3 +354,160 @@ def composite(clean_speech, processed_speech, fs): Covl = np.max((1, Covl)) Covl = np.min((5, Covl)) # limit values to [1, 5] return Csig,Cbak,Covl + + + +def fwseg_noise(clean_speech, processed_speech,sample_rate,frameLen=0.03, overlap=0.75): + + clean_length = len(clean_speech) + processed_length = len(processed_speech) + rms_all=np.linalg.norm(clean_speech)/np.sqrt(processed_length) + + winlength = round(frameLen*fs) #window length in samples + skiprate = int(np.floor((1-overlap)*frameLen*fs)) #window skip in samples + max_freq = fs/2 #maximum bandwidth + num_crit = 16 # number of critical bands + n_fft = int(2**np.ceil(np.log2(2*winlength))) + n_fftby2 = int(n_fft/2) + + cent_freq=np.zeros((num_crit,)) + bandwidth=np.zeros((num_crit,)) + + # ---------------------------------------------------------------------- + # Critical Band Filter Definitions (Center Frequency and Bandwidths in Hz) + # ---------------------------------------------------------------------- + cent_freq[0] = 150.0000; bandwidth[0] = 100.0000; + cent_freq[1] = 250.000; bandwidth[1] = 100.0000; + cent_freq[2] = 350.000; bandwidth[2] = 100.0000; + cent_freq[3] = 450.000; bandwidth[3] = 110.0000; + cent_freq[4] = 570.000; bandwidth[4] = 120.0000; + cent_freq[5] = 700.000; bandwidth[5] = 140.0000; + cent_freq[6] = 840.000; bandwidth[6] = 150.0000; + cent_freq[7] = 1000.000; bandwidth[7] = 160.000; + cent_freq[8] = 1170.000; bandwidth[8] = 190.000; + cent_freq[9] = 1370.000; bandwidth[9] = 210.000; + cent_freq[10] = 1600.000; bandwidth[10]= 240.000; + cent_freq[11] = 1850.000; bandwidth[11]= 280.000; + cent_freq[12] = 2150.000; bandwidth[12]= 320.000; + cent_freq[13] = 2500.000; bandwidth[13]= 380.000; + cent_freq[14] = 2900.000; bandwidth[14]= 450.000; + cent_freq[15] = 3400.000; bandwidth[15]= 550.000; + + Weight=np.array([0.0192,0.0312,0.0926,0.1031,0.0735,0.0611,0.0495,0.044,0.044,0.049,0.0486,0.0493, 0.049,0.0547,0.0555,0.0493]) + + # ---------------------------------------------------------------------- + # Set up the critical band filters. Note here that Gaussianly shaped + # filters are used. Also, the sum of the filter weights are equivalent + # for each critical band filter. Filter less than -30 dB and set to + # zero. + # ---------------------------------------------------------------------- + + all_f0=np.zeros((num_crit,)) + crit_filter=np.zeros((num_crit,int(n_fftby2))) + g = np.zeros((num_crit,n_fftby2)) + + b = bandwidth; + q = cent_freq/1000; + p = 4*1000*q/b; # Eq. (7) + + #15.625=4000/256 + j = np.arange(0,n_fftby2) + + for i in range(num_crit): + g[i,:]=np.abs(1-j*(sample_rate/n_fft)/(q[i]*1000));# Eq. (9) + crit_filter[i,:] = (1+p[i]*g[i,:])*np.exp(-p[i]*g[i,:]);# Eq. (8) + + num_frames = int(clean_length/skiprate-(winlength/skiprate)); # number of frames + start = 0 # starting sample + hannWin = 0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) + + f,t,clean_spec=stft(clean_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=False, boundary=None, padded=False) + f,t,processed_spec=stft(processed_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=False, boundary=None, padded=False) + + clean_frames = extractOverlappedWindows(clean_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)],winlength,winlength-skiprate,None) + rms_seg = np.linalg.norm(clean_frames,axis=-1)/np.sqrt(winlength); + rms_db = 20*np.log10(rms_seg/rms_all); + #-------------------------------------------------------------- + # cal r2_high,r2_middle,r2_low + highInd = np.where(rms_db>=0) + highInd = highInd[0] + middleInd = np.where((rms_db>=-10) & (rms_db<0)) + middleInd = middleInd[0] + lowInd = np.where(rms_db<-10) + lowInd = lowInd[0] + + num_high = np.sum(clean_spec[0:n_fftby2,highInd]*np.conj(processed_spec[0:n_fftby2,highInd]),axis=-1) + denx_high = np.sum(np.abs(clean_spec[0:n_fftby2,highInd])**2,axis=-1) + deny_high = np.sum(np.abs(processed_spec[0:n_fftby2,highInd])**2,axis=-1); + + num_middle = np.sum(clean_spec[0:n_fftby2,middleInd]*np.conj(processed_spec[0:n_fftby2,middleInd]),axis=-1) + denx_middle = np.sum(np.abs(clean_spec[0:n_fftby2,middleInd])**2,axis=-1) + deny_middle = np.sum(np.abs(processed_spec[0:n_fftby2,middleInd])**2,axis=-1); + + num_low = np.sum(clean_spec[0:n_fftby2,lowInd]*np.conj(processed_spec[0:n_fftby2,lowInd]),axis=-1) + denx_low = np.sum(np.abs(clean_spec[0:n_fftby2,lowInd])**2,axis=-1) + deny_low = np.sum(np.abs(processed_spec[0:n_fftby2,lowInd])**2,axis=-1); + + num2_high = np.abs(num_high)**2; + r2_high = num2_high/(denx_high*deny_high); + + num2_middle = np.abs(num_middle)**2; + r2_middle = num2_middle/(denx_middle*deny_middle); + + num2_low = np.abs(num_low)**2; + r2_low = num2_low/(denx_low*deny_low); + #-------------------------------------------------------------- + # cal distortion frame by frame + + clean_spec = np.abs(clean_spec); + processed_spec = np.abs(processed_spec)**2; + + W_freq=Weight + + processed_energy = crit_filter.dot((processed_spec[0:n_fftby2,highInd].T*r2_high).T) + de_processed_energy= crit_filter.dot((processed_spec[0:n_fftby2,highInd].T*(1-r2_high)).T) + SDR = processed_energy/de_processed_energy;# Eq 13 in Kates (2005) + SDRlog=10*np.log10(SDR); + SDRlog_lim = SDRlog + SDRlog_lim[SDRlog_lim<-15]=-15 + SDRlog_lim[SDRlog_lim>15]=15 # limit between [-15, 15] + Tjm = (SDRlog_lim+15)/30; + distortionh = W_freq.dot(Tjm)/np.sum(W_freq,axis=0) + distortionh[distortionh<0]=0 + + + processed_energy = crit_filter.dot((processed_spec[0:n_fftby2,middleInd].T*r2_middle).T) + de_processed_energy= crit_filter.dot((processed_spec[0:n_fftby2,middleInd].T*(1-r2_middle)).T) + SDR = processed_energy/de_processed_energy;# Eq 13 in Kates (2005) + SDRlog=10*np.log10(SDR); + SDRlog_lim = SDRlog + SDRlog_lim[SDRlog_lim<-15]=-15 + SDRlog_lim[SDRlog_lim>15]=15 # limit between [-15, 15] + Tjm = (SDRlog_lim+15)/30; + distortionm = W_freq.dot(Tjm)/np.sum(W_freq,axis=0) + distortionm[distortionm<0]=0 + + processed_energy = crit_filter.dot((processed_spec[0:n_fftby2,lowInd].T*r2_low).T) + de_processed_energy= crit_filter.dot((processed_spec[0:n_fftby2,lowInd].T*(1-r2_low)).T) + SDR = processed_energy/de_processed_energy;# Eq 13 in Kates (2005) + SDRlog=10*np.log10(SDR); + SDRlog_lim = SDRlog + SDRlog_lim[SDRlog_lim<-15]=-15 + SDRlog_lim[SDRlog_lim>15]=15 # limit between [-15, 15] + Tjm = (SDRlog_lim+15)/30; + distortionl = W_freq.dot(Tjm)/np.sum(W_freq,axis=0) + distortionl[distortionl<0]=0 + + return distortionh,distortionm,distortionl + + +def CSII(clean_speech, processed_speech,sample_rate): + sampleLen= min(len( clean_speech), len( processed_speech)) + clean_speech= clean_speech[0: sampleLen] + processed_speech= processed_speech[0: sampleLen] + vec_CSIIh,vec_CSIIm,vec_CSIIl = fwseg_noise(clean_speech, processed_speech, sample_rate) + + CSIIh=np.mean(vec_CSIIh) + CSIIm=np.mean(vec_CSIIm) + CSIIl=np.mean(vec_CSIIl) + return CSIIh,CSIIm,CSIIl \ No newline at end of file From 43061dde3de500d95f905025794ea961fa38368d Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Mon, 15 Jun 2020 18:56:03 +0200 Subject: [PATCH 25/91] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 8185037..8543c1b 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,7 @@ Python implementation of objective quality and intelligibilty measures mentioned # Implemented Speech Intelligibility Measures + Short-time objective intelligibility (stoi) ++ Coherence and speech intelligibility index (CSII) # Install with pip The setup.py is not finished yet. You have to install the dependencies by hand. From 4ca45817a48d655382be0dffbe6fc4434a70cc2b Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Tue, 16 Jun 2020 09:46:51 +0200 Subject: [PATCH 26/91] NCM metric added --- pysepm/speechEnhPerfMetrics.py | 114 ++++++++++++++++++++++++++++++++- 1 file changed, 112 insertions(+), 2 deletions(-) diff --git a/pysepm/speechEnhPerfMetrics.py b/pysepm/speechEnhPerfMetrics.py index e3989c6..b178788 100644 --- a/pysepm/speechEnhPerfMetrics.py +++ b/pysepm/speechEnhPerfMetrics.py @@ -1,5 +1,6 @@ -from scipy.signal import stft,get_window,correlate,resample +from scipy.signal import stft,get_window,correlate,resample,butter,lfilter,hilbert from scipy.linalg import solve_toeplitz,toeplitz +from scipy.interpolate import interp1d from pypesq import pypesq # https://github.com/ludlows/python-pesq import pystoi.stoi # https://github.com/mpariente/pystoi import numpy as np @@ -510,4 +511,113 @@ def CSII(clean_speech, processed_speech,sample_rate): CSIIh=np.mean(vec_CSIIh) CSIIm=np.mean(vec_CSIIm) CSIIl=np.mean(vec_CSIIl) - return CSIIh,CSIIm,CSIIl \ No newline at end of file + return CSIIh,CSIIm,CSIIl + + + +def Get_Band(M,Fs): + # This function sets the bandpass filter band edges. + # It assumes that the sampling frequency is 8000 Hz. + A = 165 + a = 2.1 + K = 1 + L = 35 + CF = 300; + x_100 = (L/a)*np.log10(CF/A + K) + CF = Fs/2-600 + x_8000 = (L/a)*np.log10(CF/A + K); + LX = x_8000 - x_100 + x_step = LX / M + x = np.arange(x_100,x_8000+x_step+1e-20,x_step) + if len(x) == M: + np.append(x,x_8000) + + BAND = A*(10**(a*x/L) - K) + return BAND + +def get_ANSIs(BAND): + fcenter=(BAND[0:-1]+BAND[1:])/2; + + # Data from Table B.1 in "ANSI (1997). S3.5–1997 Methods for Calculation of the Speech Intelligibility + # Index. New York: American National Standards Institute." + f=np.array([150,250,350,450,570,700,840,1000,1170,1370,1600,1850,2150,2500,2900,3400,4000,4800,5800,7000,8500]) + BIF=np.array([0.0192,0.0312,0.0926,0.1031,0.0735,0.0611,0.0495,0.0440,0.0440,0.0490,0.0486,0.0493,0.0490,0.0547,0.0555,0.0493,0.0359,0.0387,0.0256,0.0219,0.0043]) + f_ANSI = interp1d(f,BIF) + ANSIs= f_ANSI(fcenter); + return fcenter,ANSIs + + +def NCM(clean_speech,processed_speech,fs): + + x= clean_speech # clean signal + y= processed_speech # noisy signal + F_SIGNAL = fs + + F_ENVELOPE = 32 # limits modulations to 0 Ly: + x = x[0:Ly] + if Ly > Lx: + y = y[0:Lx] + + Lx = len(x); + Ly = len(y); + + X_BANDS = np.zeros((Lx,M_CHANNELS)) + Y_BANDS = np.zeros((Lx,M_CHANNELS)) + + # DESIGN BANDPASS FILTERS + for a in range(M_CHANNELS): + B_bp,A_bp = butter( 4 , np.array([BAND[a],BAND[a+1]])*(2/F_SIGNAL),btype='bandpass') + X_BANDS[:,a] = lfilter( B_bp , A_bp , x ) + Y_BANDS[:,a] = lfilter( B_bp , A_bp , y ) + + # CALCULATE HILBERT ENVELOPES, and resample at F_ENVELOPE Hz + analytic_x = hilbert( X_BANDS,axis=0); + X = np.abs( analytic_x ); + X = resample( X , round(len(x)/F_SIGNAL*F_ENVELOPE)); + + analytic_y = hilbert( Y_BANDS,axis=0); + Y = np.abs( analytic_y ); + Y = resample( Y , round(len(x)/F_SIGNAL*F_ENVELOPE)); + + ## ---compute weights based on clean signal's rms envelopes----- + # + Ldx, pp=X.shape + p=3 # power exponent - see Eq. 12 + + ro2 = np.zeros((M_CHANNELS,)) + asnr = np.zeros((M_CHANNELS,)) + TI = np.zeros((M_CHANNELS,)) + + for k in range(M_CHANNELS): + x_tmp= X[ :, k] + y_tmp= Y[ :, k] + lambda_x= np.linalg.norm( x_tmp- np.mean( x_tmp))**2 + lambda_y= np.linalg.norm( y_tmp- np.mean( y_tmp))**2 + lambda_xy= np.sum( (x_tmp- np.mean( x_tmp))*(y_tmp- np.mean( y_tmp))) + ro2[k]= (lambda_xy**2)/ (lambda_x*lambda_y) + asnr[k]= 10*np.log10( (ro2[k]+ 1e-20)/ (1- ro2[k]+ 1e-20)); # Eq.9 in [1] + + if asnr[k]< -15: + asnr[k]= -15 + elif asnr[k]> 15: + asnr[k]= 15 + + TI[k]= (asnr[k]+ 15)/ 30 # Eq.10 in [1] + + ncm_val= WEIGHT.dot(TI)/np.sum(WEIGHT) # Eq.11 + return ncm_val \ No newline at end of file From 833685e9f4b8e55d09034d29829c7cd054d24cb9 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 16 Jun 2020 09:50:04 +0200 Subject: [PATCH 27/91] Update README.md --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/README.md b/README.md index 8543c1b..087c1a7 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,7 @@ Python implementation of objective quality and intelligibilty measures mentioned # Implemented Speech Intelligibility Measures + Short-time objective intelligibility (stoi) + Coherence and speech intelligibility index (CSII) ++ Normalized-covariance measure (NCM) # Install with pip The setup.py is not finished yet. You have to install the dependencies by hand. @@ -39,3 +40,9 @@ Finally install pysepm: ``` pip3 install https://github.com/schmiph2/pysepm/archive/master.zip ``` + + +# TODO: ++ Finish setup script ++ systematic tests for all measures ++ change resample method used (e.g. in NCM) to fit matlab implementation From b3b20f7cc73ad146cbc58498e1e158ab9247a92c Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 16 Jun 2020 09:55:00 +0200 Subject: [PATCH 28/91] Update README.md --- README.md | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 087c1a7..dced8fb 100644 --- a/README.md +++ b/README.md @@ -14,16 +14,22 @@ Python implementation of objective quality and intelligibilty measures mentioned # Implemented Speech Quality Measures + Segmental Signal-to-Noise Ratio (SNRseg) + Frequency-weighted Segmental SNR (fwSNRseg) -+ Log-likelihood Ratio (llr) -+ Weighted Spectral Slope (wss) -+ Perceptual Evaluation of Speech Quality (pesq) ++ Log-likelihood Ratio (LLR) ++ Weighted Spectral Slope (WSS) ++ Perceptual Evaluation of Speech Quality (PESQ) + Composite Objective Speech Quality (composite) # Implemented Speech Intelligibility Measures -+ Short-time objective intelligibility (stoi) ++ Short-time objective intelligibility (STOI) + Coherence and speech intelligibility index (CSII) + Normalized-covariance measure (NCM) +# Dereverberation Measures (TOD0) ++ Signal to reverberant ratio (SRR) ++ Reverberation decay tail (RDT) ++ Bark spectral distortion (BSD) ++ Speech to reverberation modulation ratio (SRMR) + # Install with pip The setup.py is not finished yet. You have to install the dependencies by hand. From 3a06d40a15979838ae76db53f9d0ea7754c2b3d9 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 16 Jun 2020 09:56:39 +0200 Subject: [PATCH 29/91] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index dced8fb..42d60c9 100644 --- a/README.md +++ b/README.md @@ -10,8 +10,8 @@ Python implementation of objective quality and intelligibilty measures mentioned scipy pystoi pypesq - -# Implemented Speech Quality Measures +# Implemented Measures +## Speech Quality Measures + Segmental Signal-to-Noise Ratio (SNRseg) + Frequency-weighted Segmental SNR (fwSNRseg) + Log-likelihood Ratio (LLR) @@ -19,12 +19,12 @@ Python implementation of objective quality and intelligibilty measures mentioned + Perceptual Evaluation of Speech Quality (PESQ) + Composite Objective Speech Quality (composite) -# Implemented Speech Intelligibility Measures +## Speech Intelligibility Measures + Short-time objective intelligibility (STOI) + Coherence and speech intelligibility index (CSII) + Normalized-covariance measure (NCM) -# Dereverberation Measures (TOD0) +## Dereverberation Measures (TOD0) + Signal to reverberant ratio (SRR) + Reverberation decay tail (RDT) + Bark spectral distortion (BSD) From de2dd845ec2a617f30363d35dc943266c8f02958 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 16 Jun 2020 13:13:40 +0200 Subject: [PATCH 30/91] Create requirements.txt --- requirements.txt | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..a11602f --- /dev/null +++ b/requirements.txt @@ -0,0 +1,5 @@ +cython +scipy +numpy +pesq +pystoi From 0112c7591c4c613a816f02d992a698bc08dba8db Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 16 Jun 2020 13:32:43 +0200 Subject: [PATCH 31/91] Update requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index a11602f..4759d30 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ cython +wheel scipy numpy pesq From e717f173263d213ead8e058d39b39749fc754b7b Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 16 Jun 2020 13:33:57 +0200 Subject: [PATCH 32/91] Update setup.py --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index f4a3bb2..6af594e 100644 --- a/setup.py +++ b/setup.py @@ -11,8 +11,8 @@ license='MIT', install_requires=['numpy', 'scipy', - 'pystoi @ https://github.com/schmiph2/pystoi/archive/master.zip#egg=pystoi-1.0.0', - 'pypesq @ https://github.com/schmiph2/python-pesq/archive/master.zip#egg=pypesq-1.0.0', + 'pystoi', + 'pesq', ], classifiers=[ 'Development Status :: 4 - Beta', From c885443ba086750763b5a254a74bd2d13109b401 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 16 Jun 2020 13:34:24 +0200 Subject: [PATCH 33/91] Update setup.py --- setup.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 6af594e..415acdc 100644 --- a/setup.py +++ b/setup.py @@ -9,8 +9,11 @@ author_email='scdp@zhaw.ch', url='https://github.zhaw.ch/scdp/py-sepm', license='MIT', - install_requires=['numpy', - 'scipy', + install_requires=[ + 'cython', + 'wheel', + 'numpy', + 'scipy', 'pystoi', 'pesq', ], From 3065522827ede61547249715aa61998b626c592c Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 16 Jun 2020 13:35:29 +0200 Subject: [PATCH 34/91] Delete requirements.txt --- requirements.txt | 6 ------ 1 file changed, 6 deletions(-) delete mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 4759d30..0000000 --- a/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -cython -wheel -scipy -numpy -pesq -pystoi From 173a2940f160f95591f1a8f8aac72a8e9297bb54 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 16 Jun 2020 13:51:22 +0200 Subject: [PATCH 35/91] Create pyproject.toml --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 pyproject.toml diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..bc2be67 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,2 @@ +[build-system] +requires = ["setuptools", "wheel", "Cython"] From 713c15ff5b4f59ec2218c084149e93aaf8a54f96 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 16 Jun 2020 13:51:42 +0200 Subject: [PATCH 36/91] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index bc2be67..f1a89dd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,2 +1,2 @@ [build-system] -requires = ["setuptools", "wheel", "Cython"] +requires = ["setuptools", "wheel", "Cython","numpy"] From beaba4eb8aec46fd864f9246cb7bb912ea0cdc51 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Wed, 17 Jun 2020 17:04:13 +0200 Subject: [PATCH 37/91] restructred project and added some reverberation metrics --- pysepm/intelligibilityMeasures.py | 287 +++++++++++++++++ ...chEnhPerfMetrics.py => qualityMeasures.py} | 302 +----------------- 2 files changed, 299 insertions(+), 290 deletions(-) create mode 100644 pysepm/intelligibilityMeasures.py rename pysepm/{speechEnhPerfMetrics.py => qualityMeasures.py} (54%) diff --git a/pysepm/intelligibilityMeasures.py b/pysepm/intelligibilityMeasures.py new file mode 100644 index 0000000..7752148 --- /dev/null +++ b/pysepm/intelligibilityMeasures.py @@ -0,0 +1,287 @@ +from scipy.signal import stft,resample,butter,lfilter,hilbert +from scipy.interpolate import interp1d +import pystoi.stoi # https://github.com/mpariente/pystoi +import numpy as np + +from .util import extract_overlapped_windows + +stoi = pystoi.stoi.stoi + +def fwseg_noise(clean_speech, processed_speech,fs,frameLen=0.03, overlap=0.75): + + clean_length = len(clean_speech) + processed_length = len(processed_speech) + rms_all=np.linalg.norm(clean_speech)/np.sqrt(processed_length) + + winlength = round(frameLen*fs) #window length in samples + skiprate = int(np.floor((1-overlap)*frameLen*fs)) #window skip in samples + max_freq = fs/2 #maximum bandwidth + num_crit = 16 # number of critical bands + n_fft = int(2**np.ceil(np.log2(2*winlength))) + n_fftby2 = int(n_fft/2) + + cent_freq=np.zeros((num_crit,)) + bandwidth=np.zeros((num_crit,)) + + # ---------------------------------------------------------------------- + # Critical Band Filter Definitions (Center Frequency and Bandwidths in Hz) + # ---------------------------------------------------------------------- + cent_freq[0] = 150.0000; bandwidth[0] = 100.0000; + cent_freq[1] = 250.000; bandwidth[1] = 100.0000; + cent_freq[2] = 350.000; bandwidth[2] = 100.0000; + cent_freq[3] = 450.000; bandwidth[3] = 110.0000; + cent_freq[4] = 570.000; bandwidth[4] = 120.0000; + cent_freq[5] = 700.000; bandwidth[5] = 140.0000; + cent_freq[6] = 840.000; bandwidth[6] = 150.0000; + cent_freq[7] = 1000.000; bandwidth[7] = 160.000; + cent_freq[8] = 1170.000; bandwidth[8] = 190.000; + cent_freq[9] = 1370.000; bandwidth[9] = 210.000; + cent_freq[10] = 1600.000; bandwidth[10]= 240.000; + cent_freq[11] = 1850.000; bandwidth[11]= 280.000; + cent_freq[12] = 2150.000; bandwidth[12]= 320.000; + cent_freq[13] = 2500.000; bandwidth[13]= 380.000; + cent_freq[14] = 2900.000; bandwidth[14]= 450.000; + cent_freq[15] = 3400.000; bandwidth[15]= 550.000; + + Weight=np.array([0.0192,0.0312,0.0926,0.1031,0.0735,0.0611,0.0495,0.044,0.044,0.049,0.0486,0.0493, 0.049,0.0547,0.0555,0.0493]) + + # ---------------------------------------------------------------------- + # Set up the critical band filters. Note here that Gaussianly shaped + # filters are used. Also, the sum of the filter weights are equivalent + # for each critical band filter. Filter less than -30 dB and set to + # zero. + # ---------------------------------------------------------------------- + + all_f0=np.zeros((num_crit,)) + crit_filter=np.zeros((num_crit,int(n_fftby2))) + g = np.zeros((num_crit,n_fftby2)) + + b = bandwidth; + q = cent_freq/1000; + p = 4*1000*q/b; # Eq. (7) + + #15.625=4000/256 + j = np.arange(0,n_fftby2) + + for i in range(num_crit): + g[i,:]=np.abs(1-j*(fs/n_fft)/(q[i]*1000));# Eq. (9) + crit_filter[i,:] = (1+p[i]*g[i,:])*np.exp(-p[i]*g[i,:]);# Eq. (8) + + num_frames = int(clean_length/skiprate-(winlength/skiprate)); # number of frames + start = 0 # starting sample + hannWin = 0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) + + f,t,clean_spec=stft(clean_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=False, boundary=None, padded=False) + f,t,processed_spec=stft(processed_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=False, boundary=None, padded=False) + + clean_frames = extract_overlapped_windows(clean_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)],winlength,winlength-skiprate,None) + rms_seg = np.linalg.norm(clean_frames,axis=-1)/np.sqrt(winlength); + rms_db = 20*np.log10(rms_seg/rms_all); + #-------------------------------------------------------------- + # cal r2_high,r2_middle,r2_low + highInd = np.where(rms_db>=0) + highInd = highInd[0] + middleInd = np.where((rms_db>=-10) & (rms_db<0)) + middleInd = middleInd[0] + lowInd = np.where(rms_db<-10) + lowInd = lowInd[0] + + num_high = np.sum(clean_spec[0:n_fftby2,highInd]*np.conj(processed_spec[0:n_fftby2,highInd]),axis=-1) + denx_high = np.sum(np.abs(clean_spec[0:n_fftby2,highInd])**2,axis=-1) + deny_high = np.sum(np.abs(processed_spec[0:n_fftby2,highInd])**2,axis=-1); + + num_middle = np.sum(clean_spec[0:n_fftby2,middleInd]*np.conj(processed_spec[0:n_fftby2,middleInd]),axis=-1) + denx_middle = np.sum(np.abs(clean_spec[0:n_fftby2,middleInd])**2,axis=-1) + deny_middle = np.sum(np.abs(processed_spec[0:n_fftby2,middleInd])**2,axis=-1); + + num_low = np.sum(clean_spec[0:n_fftby2,lowInd]*np.conj(processed_spec[0:n_fftby2,lowInd]),axis=-1) + denx_low = np.sum(np.abs(clean_spec[0:n_fftby2,lowInd])**2,axis=-1) + deny_low = np.sum(np.abs(processed_spec[0:n_fftby2,lowInd])**2,axis=-1); + + num2_high = np.abs(num_high)**2; + r2_high = num2_high/(denx_high*deny_high); + + num2_middle = np.abs(num_middle)**2; + r2_middle = num2_middle/(denx_middle*deny_middle); + + num2_low = np.abs(num_low)**2; + r2_low = num2_low/(denx_low*deny_low); + #-------------------------------------------------------------- + # cal distortion frame by frame + + clean_spec = np.abs(clean_spec); + processed_spec = np.abs(processed_spec)**2; + + W_freq=Weight + + processed_energy = crit_filter.dot((processed_spec[0:n_fftby2,highInd].T*r2_high).T) + de_processed_energy= crit_filter.dot((processed_spec[0:n_fftby2,highInd].T*(1-r2_high)).T) + SDR = processed_energy/de_processed_energy;# Eq 13 in Kates (2005) + SDRlog=10*np.log10(SDR); + SDRlog_lim = SDRlog + SDRlog_lim[SDRlog_lim<-15]=-15 + SDRlog_lim[SDRlog_lim>15]=15 # limit between [-15, 15] + Tjm = (SDRlog_lim+15)/30; + distortionh = W_freq.dot(Tjm)/np.sum(W_freq,axis=0) + distortionh[distortionh<0]=0 + + + processed_energy = crit_filter.dot((processed_spec[0:n_fftby2,middleInd].T*r2_middle).T) + de_processed_energy= crit_filter.dot((processed_spec[0:n_fftby2,middleInd].T*(1-r2_middle)).T) + SDR = processed_energy/de_processed_energy;# Eq 13 in Kates (2005) + SDRlog=10*np.log10(SDR); + SDRlog_lim = SDRlog + SDRlog_lim[SDRlog_lim<-15]=-15 + SDRlog_lim[SDRlog_lim>15]=15 # limit between [-15, 15] + Tjm = (SDRlog_lim+15)/30; + distortionm = W_freq.dot(Tjm)/np.sum(W_freq,axis=0) + distortionm[distortionm<0]=0 + + processed_energy = crit_filter.dot((processed_spec[0:n_fftby2,lowInd].T*r2_low).T) + de_processed_energy= crit_filter.dot((processed_spec[0:n_fftby2,lowInd].T*(1-r2_low)).T) + SDR = processed_energy/de_processed_energy;# Eq 13 in Kates (2005) + SDRlog=10*np.log10(SDR); + SDRlog_lim = SDRlog + SDRlog_lim[SDRlog_lim<-15]=-15 + SDRlog_lim[SDRlog_lim>15]=15 # limit between [-15, 15] + Tjm = (SDRlog_lim+15)/30; + distortionl = W_freq.dot(Tjm)/np.sum(W_freq,axis=0) + distortionl[distortionl<0]=0 + + return distortionh,distortionm,distortionl + + +def csii(clean_speech, processed_speech,sample_rate): + sampleLen= min(len( clean_speech), len( processed_speech)) + clean_speech= clean_speech[0: sampleLen] + processed_speech= processed_speech[0: sampleLen] + vec_CSIIh,vec_CSIIm,vec_CSIIl = fwseg_noise(clean_speech, processed_speech, sample_rate) + + CSIIh=np.mean(vec_CSIIh) + CSIIm=np.mean(vec_CSIIm) + CSIIl=np.mean(vec_CSIIl) + return CSIIh,CSIIm,CSIIl + + + +def get_band(M,Fs): + # This function sets the bandpass filter band edges. + # It assumes that the sampling frequency is 8000 Hz. + A = 165 + a = 2.1 + K = 1 + L = 35 + CF = 300; + x_100 = (L/a)*np.log10(CF/A + K) + CF = Fs/2-600 + x_8000 = (L/a)*np.log10(CF/A + K); + LX = x_8000 - x_100 + x_step = LX / M + x = np.arange(x_100,x_8000+x_step+1e-20,x_step) + if len(x) == M: + np.append(x,x_8000) + + BAND = A*(10**(a*x/L) - K) + return BAND + +def get_ansis(BAND): + fcenter=(BAND[0:-1]+BAND[1:])/2; + + # Data from Table B.1 in "ANSI (1997). S3.5–1997 Methods for Calculation of the Speech Intelligibility + # Index. New York: American National Standards Institute." + f=np.array([150,250,350,450,570,700,840,1000,1170,1370,1600,1850,2150,2500,2900,3400,4000,4800,5800,7000,8500]) + BIF=np.array([0.0192,0.0312,0.0926,0.1031,0.0735,0.0611,0.0495,0.0440,0.0440,0.0490,0.0486,0.0493,0.0490,0.0547,0.0555,0.0493,0.0359,0.0387,0.0256,0.0219,0.0043]) + f_ANSI = interp1d(f,BIF) + ANSIs= f_ANSI(fcenter); + return fcenter,ANSIs + + +def ncm(clean_speech,processed_speech,fs): + + if fs != 8000 and fs != 16000: + if fs >= 16000: + numSamples=round(len(clean_speech)/fs*16000) + clean_speech = resample(clean_speech, numSamples) + numSamples=round(len(processed_speech)/fs*16000) + processed_speech = resample(processed_speech, numSamples) + fs = 16000 + else: + numSamples=round(len(clean_speech)/fs*8000) + clean_speech = resample(clean_speech, numSamples) + numSamples=round(len(processed_speech)/fs*8000) + processed_speech = resample(processed_speech, numSamples) + fs = 8000 + + + x= clean_speech # clean signal + y= processed_speech # noisy signal + F_SIGNAL = fs + + F_ENVELOPE = 32 # limits modulations to 0 Ly: + x = x[0:Ly] + if Ly > Lx: + y = y[0:Lx] + + Lx = len(x); + Ly = len(y); + + X_BANDS = np.zeros((Lx,M_CHANNELS)) + Y_BANDS = np.zeros((Lx,M_CHANNELS)) + + # DESIGN BANDPASS FILTERS + for a in range(M_CHANNELS): + B_bp,A_bp = butter( 4 , np.array([BAND[a],BAND[a+1]])*(2/F_SIGNAL),btype='bandpass') + X_BANDS[:,a] = lfilter( B_bp , A_bp , x ) + Y_BANDS[:,a] = lfilter( B_bp , A_bp , y ) + + # CALCULATE HILBERT ENVELOPES, and resample at F_ENVELOPE Hz + analytic_x = hilbert( X_BANDS,axis=0); + X = np.abs( analytic_x ); + X = resample( X , round(len(x)/F_SIGNAL*F_ENVELOPE)); + + analytic_y = hilbert( Y_BANDS,axis=0); + Y = np.abs( analytic_y ); + Y = resample( Y , round(len(x)/F_SIGNAL*F_ENVELOPE)); + + ## ---compute weights based on clean signal's rms envelopes----- + # + Ldx, pp=X.shape + p=3 # power exponent - see Eq. 12 + + ro2 = np.zeros((M_CHANNELS,)) + asnr = np.zeros((M_CHANNELS,)) + TI = np.zeros((M_CHANNELS,)) + + for k in range(M_CHANNELS): + x_tmp= X[ :, k] + y_tmp= Y[ :, k] + lambda_x= np.linalg.norm( x_tmp- np.mean( x_tmp))**2 + lambda_y= np.linalg.norm( y_tmp- np.mean( y_tmp))**2 + lambda_xy= np.sum( (x_tmp- np.mean( x_tmp))*(y_tmp- np.mean( y_tmp))) + ro2[k]= (lambda_xy**2)/ (lambda_x*lambda_y) + asnr[k]= 10*np.log10( (ro2[k]+ 1e-20)/ (1- ro2[k]+ 1e-20)); # Eq.9 in [1] + + if asnr[k]< -15: + asnr[k]= -15 + elif asnr[k]> 15: + asnr[k]= 15 + + TI[k]= (asnr[k]+ 15)/ 30 # Eq.10 in [1] + + ncm_val= WEIGHT.dot(TI)/np.sum(WEIGHT) # Eq.11 + return ncm_val \ No newline at end of file diff --git a/pysepm/speechEnhPerfMetrics.py b/pysepm/qualityMeasures.py similarity index 54% rename from pysepm/speechEnhPerfMetrics.py rename to pysepm/qualityMeasures.py index b178788..a9a51f9 100644 --- a/pysepm/speechEnhPerfMetrics.py +++ b/pysepm/qualityMeasures.py @@ -1,23 +1,9 @@ -from scipy.signal import stft,get_window,correlate,resample,butter,lfilter,hilbert +from scipy.signal import stft,get_window,correlate,resample from scipy.linalg import solve_toeplitz,toeplitz -from scipy.interpolate import interp1d from pypesq import pypesq # https://github.com/ludlows/python-pesq -import pystoi.stoi # https://github.com/mpariente/pystoi import numpy as np - -stoi = pystoi.stoi.stoi - -def extractOverlappedWindows(x,nperseg,noverlap,window=None): - # source: https://github.com/scipy/scipy/blob/v1.2.1/scipy/signal/spectral.py - step = nperseg - noverlap - shape = x.shape[:-1]+((x.shape[-1]-noverlap)//step, nperseg) - strides = x.strides[:-1]+(step*x.strides[-1], x.strides[-1]) - result = np.lib.stride_tricks.as_strided(x, shape=shape, - strides=strides) - if window is not None: - result = window * result - return result +from .util import extract_overlapped_windows def SNRseg(clean_speech, processed_speech,fs, frameLen=0.03, overlap=0.75): eps=np.finfo(np.float64).eps @@ -28,8 +14,8 @@ def SNRseg(clean_speech, processed_speech,fs, frameLen=0.03, overlap=0.75): MAX_SNR = 35 # maximum SNR in dB hannWin=0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) - clean_speech_framed=extractOverlappedWindows(clean_speech,winlength,winlength-skiprate,hannWin) - processed_speech_framed=extractOverlappedWindows(processed_speech,winlength,winlength-skiprate,hannWin) + clean_speech_framed=extract_overlapped_windows(clean_speech,winlength,winlength-skiprate,hannWin) + processed_speech_framed=extract_overlapped_windows(processed_speech,winlength,winlength-skiprate,hannWin) signal_energy = np.power(clean_speech_framed,2).sum(-1) noise_energy = np.power(clean_speech_framed-processed_speech_framed,2).sum(-1) @@ -159,8 +145,8 @@ def llr(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): P = 16 # this could vary depending on sampling frequency. hannWin=0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) - clean_speech_framed=extractOverlappedWindows(clean_speech,winlength,winlength-skiprate,hannWin) - processed_speech_framed=extractOverlappedWindows(processed_speech,winlength,winlength-skiprate,hannWin) + clean_speech_framed=extract_overlapped_windows(clean_speech,winlength,winlength-skiprate,hannWin) + processed_speech_framed=extract_overlapped_windows(processed_speech,winlength,winlength-skiprate,hannWin) numFrames=clean_speech_framed.shape[0] numerators = np.zeros((numFrames-1,)) denominators = np.zeros((numFrames-1,)) @@ -182,7 +168,7 @@ def llr(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): return np.mean(distortion) -def findLocPeaks(slope,energy): +def find_loc_peaks(slope,energy): num_crit = len(energy) loc_peaks=np.zeros_like(slope) @@ -300,8 +286,8 @@ def wss(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): clean_loc_peaks=np.zeros_like(log_clean_energy_slope) proc_loc_peaks=np.zeros_like(log_proc_energy_slope) for ii in range(numFrames): - clean_loc_peaks[:,ii]=findLocPeaks(log_clean_energy_slope[:,ii],log_clean_energy[:,ii]) - proc_loc_peaks[:,ii]=findLocPeaks(log_proc_energy_slope[:,ii],log_proc_energy[:,ii]) + clean_loc_peaks[:,ii]=find_loc_peaks(log_clean_energy_slope[:,ii],log_clean_energy[:,ii]) + proc_loc_peaks[:,ii]=find_loc_peaks(log_proc_energy_slope[:,ii],log_proc_energy[:,ii]) Wmax_clean = Kmax / (Kmax + dBMax_clean - log_clean_energy[:-1,:]) @@ -329,9 +315,11 @@ def pesq(clean_speech, processed_speech, fs): pesq_mos = pypesq(fs,clean_speech, processed_speech, 'wb') elif fs >= 16000: numSamples=round(len(clean_speech)/fs*16000) + fs = 16000 pesq_mos = pypesq(fs,resample(clean_speech, numSamples), resample(processed_speech, numSamples), 'wb') else: numSamples=round(len(clean_speech)/fs*8000) + fs = 8000 pesq_mos = pypesq(fs,resample(clean_speech, numSamples), resample(processed_speech, numSamples), 'nb') pesq_mos = 46607/14945 - (2000*np.log(1/(pesq_mos/4 - 999/4000) - 1))/2989 #remap to raw pesq score @@ -354,270 +342,4 @@ def composite(clean_speech, processed_speech, fs): Covl = 1.594 + 0.805*pesq_mos - 0.512*llr_mean - 0.007*wss_dist Covl = np.max((1, Covl)) Covl = np.min((5, Covl)) # limit values to [1, 5] - return Csig,Cbak,Covl - - - -def fwseg_noise(clean_speech, processed_speech,sample_rate,frameLen=0.03, overlap=0.75): - - clean_length = len(clean_speech) - processed_length = len(processed_speech) - rms_all=np.linalg.norm(clean_speech)/np.sqrt(processed_length) - - winlength = round(frameLen*fs) #window length in samples - skiprate = int(np.floor((1-overlap)*frameLen*fs)) #window skip in samples - max_freq = fs/2 #maximum bandwidth - num_crit = 16 # number of critical bands - n_fft = int(2**np.ceil(np.log2(2*winlength))) - n_fftby2 = int(n_fft/2) - - cent_freq=np.zeros((num_crit,)) - bandwidth=np.zeros((num_crit,)) - - # ---------------------------------------------------------------------- - # Critical Band Filter Definitions (Center Frequency and Bandwidths in Hz) - # ---------------------------------------------------------------------- - cent_freq[0] = 150.0000; bandwidth[0] = 100.0000; - cent_freq[1] = 250.000; bandwidth[1] = 100.0000; - cent_freq[2] = 350.000; bandwidth[2] = 100.0000; - cent_freq[3] = 450.000; bandwidth[3] = 110.0000; - cent_freq[4] = 570.000; bandwidth[4] = 120.0000; - cent_freq[5] = 700.000; bandwidth[5] = 140.0000; - cent_freq[6] = 840.000; bandwidth[6] = 150.0000; - cent_freq[7] = 1000.000; bandwidth[7] = 160.000; - cent_freq[8] = 1170.000; bandwidth[8] = 190.000; - cent_freq[9] = 1370.000; bandwidth[9] = 210.000; - cent_freq[10] = 1600.000; bandwidth[10]= 240.000; - cent_freq[11] = 1850.000; bandwidth[11]= 280.000; - cent_freq[12] = 2150.000; bandwidth[12]= 320.000; - cent_freq[13] = 2500.000; bandwidth[13]= 380.000; - cent_freq[14] = 2900.000; bandwidth[14]= 450.000; - cent_freq[15] = 3400.000; bandwidth[15]= 550.000; - - Weight=np.array([0.0192,0.0312,0.0926,0.1031,0.0735,0.0611,0.0495,0.044,0.044,0.049,0.0486,0.0493, 0.049,0.0547,0.0555,0.0493]) - - # ---------------------------------------------------------------------- - # Set up the critical band filters. Note here that Gaussianly shaped - # filters are used. Also, the sum of the filter weights are equivalent - # for each critical band filter. Filter less than -30 dB and set to - # zero. - # ---------------------------------------------------------------------- - - all_f0=np.zeros((num_crit,)) - crit_filter=np.zeros((num_crit,int(n_fftby2))) - g = np.zeros((num_crit,n_fftby2)) - - b = bandwidth; - q = cent_freq/1000; - p = 4*1000*q/b; # Eq. (7) - - #15.625=4000/256 - j = np.arange(0,n_fftby2) - - for i in range(num_crit): - g[i,:]=np.abs(1-j*(sample_rate/n_fft)/(q[i]*1000));# Eq. (9) - crit_filter[i,:] = (1+p[i]*g[i,:])*np.exp(-p[i]*g[i,:]);# Eq. (8) - - num_frames = int(clean_length/skiprate-(winlength/skiprate)); # number of frames - start = 0 # starting sample - hannWin = 0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) - - f,t,clean_spec=stft(clean_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=False, boundary=None, padded=False) - f,t,processed_spec=stft(processed_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=False, boundary=None, padded=False) - - clean_frames = extractOverlappedWindows(clean_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)],winlength,winlength-skiprate,None) - rms_seg = np.linalg.norm(clean_frames,axis=-1)/np.sqrt(winlength); - rms_db = 20*np.log10(rms_seg/rms_all); - #-------------------------------------------------------------- - # cal r2_high,r2_middle,r2_low - highInd = np.where(rms_db>=0) - highInd = highInd[0] - middleInd = np.where((rms_db>=-10) & (rms_db<0)) - middleInd = middleInd[0] - lowInd = np.where(rms_db<-10) - lowInd = lowInd[0] - - num_high = np.sum(clean_spec[0:n_fftby2,highInd]*np.conj(processed_spec[0:n_fftby2,highInd]),axis=-1) - denx_high = np.sum(np.abs(clean_spec[0:n_fftby2,highInd])**2,axis=-1) - deny_high = np.sum(np.abs(processed_spec[0:n_fftby2,highInd])**2,axis=-1); - - num_middle = np.sum(clean_spec[0:n_fftby2,middleInd]*np.conj(processed_spec[0:n_fftby2,middleInd]),axis=-1) - denx_middle = np.sum(np.abs(clean_spec[0:n_fftby2,middleInd])**2,axis=-1) - deny_middle = np.sum(np.abs(processed_spec[0:n_fftby2,middleInd])**2,axis=-1); - - num_low = np.sum(clean_spec[0:n_fftby2,lowInd]*np.conj(processed_spec[0:n_fftby2,lowInd]),axis=-1) - denx_low = np.sum(np.abs(clean_spec[0:n_fftby2,lowInd])**2,axis=-1) - deny_low = np.sum(np.abs(processed_spec[0:n_fftby2,lowInd])**2,axis=-1); - - num2_high = np.abs(num_high)**2; - r2_high = num2_high/(denx_high*deny_high); - - num2_middle = np.abs(num_middle)**2; - r2_middle = num2_middle/(denx_middle*deny_middle); - - num2_low = np.abs(num_low)**2; - r2_low = num2_low/(denx_low*deny_low); - #-------------------------------------------------------------- - # cal distortion frame by frame - - clean_spec = np.abs(clean_spec); - processed_spec = np.abs(processed_spec)**2; - - W_freq=Weight - - processed_energy = crit_filter.dot((processed_spec[0:n_fftby2,highInd].T*r2_high).T) - de_processed_energy= crit_filter.dot((processed_spec[0:n_fftby2,highInd].T*(1-r2_high)).T) - SDR = processed_energy/de_processed_energy;# Eq 13 in Kates (2005) - SDRlog=10*np.log10(SDR); - SDRlog_lim = SDRlog - SDRlog_lim[SDRlog_lim<-15]=-15 - SDRlog_lim[SDRlog_lim>15]=15 # limit between [-15, 15] - Tjm = (SDRlog_lim+15)/30; - distortionh = W_freq.dot(Tjm)/np.sum(W_freq,axis=0) - distortionh[distortionh<0]=0 - - - processed_energy = crit_filter.dot((processed_spec[0:n_fftby2,middleInd].T*r2_middle).T) - de_processed_energy= crit_filter.dot((processed_spec[0:n_fftby2,middleInd].T*(1-r2_middle)).T) - SDR = processed_energy/de_processed_energy;# Eq 13 in Kates (2005) - SDRlog=10*np.log10(SDR); - SDRlog_lim = SDRlog - SDRlog_lim[SDRlog_lim<-15]=-15 - SDRlog_lim[SDRlog_lim>15]=15 # limit between [-15, 15] - Tjm = (SDRlog_lim+15)/30; - distortionm = W_freq.dot(Tjm)/np.sum(W_freq,axis=0) - distortionm[distortionm<0]=0 - - processed_energy = crit_filter.dot((processed_spec[0:n_fftby2,lowInd].T*r2_low).T) - de_processed_energy= crit_filter.dot((processed_spec[0:n_fftby2,lowInd].T*(1-r2_low)).T) - SDR = processed_energy/de_processed_energy;# Eq 13 in Kates (2005) - SDRlog=10*np.log10(SDR); - SDRlog_lim = SDRlog - SDRlog_lim[SDRlog_lim<-15]=-15 - SDRlog_lim[SDRlog_lim>15]=15 # limit between [-15, 15] - Tjm = (SDRlog_lim+15)/30; - distortionl = W_freq.dot(Tjm)/np.sum(W_freq,axis=0) - distortionl[distortionl<0]=0 - - return distortionh,distortionm,distortionl - - -def CSII(clean_speech, processed_speech,sample_rate): - sampleLen= min(len( clean_speech), len( processed_speech)) - clean_speech= clean_speech[0: sampleLen] - processed_speech= processed_speech[0: sampleLen] - vec_CSIIh,vec_CSIIm,vec_CSIIl = fwseg_noise(clean_speech, processed_speech, sample_rate) - - CSIIh=np.mean(vec_CSIIh) - CSIIm=np.mean(vec_CSIIm) - CSIIl=np.mean(vec_CSIIl) - return CSIIh,CSIIm,CSIIl - - - -def Get_Band(M,Fs): - # This function sets the bandpass filter band edges. - # It assumes that the sampling frequency is 8000 Hz. - A = 165 - a = 2.1 - K = 1 - L = 35 - CF = 300; - x_100 = (L/a)*np.log10(CF/A + K) - CF = Fs/2-600 - x_8000 = (L/a)*np.log10(CF/A + K); - LX = x_8000 - x_100 - x_step = LX / M - x = np.arange(x_100,x_8000+x_step+1e-20,x_step) - if len(x) == M: - np.append(x,x_8000) - - BAND = A*(10**(a*x/L) - K) - return BAND - -def get_ANSIs(BAND): - fcenter=(BAND[0:-1]+BAND[1:])/2; - - # Data from Table B.1 in "ANSI (1997). S3.5–1997 Methods for Calculation of the Speech Intelligibility - # Index. New York: American National Standards Institute." - f=np.array([150,250,350,450,570,700,840,1000,1170,1370,1600,1850,2150,2500,2900,3400,4000,4800,5800,7000,8500]) - BIF=np.array([0.0192,0.0312,0.0926,0.1031,0.0735,0.0611,0.0495,0.0440,0.0440,0.0490,0.0486,0.0493,0.0490,0.0547,0.0555,0.0493,0.0359,0.0387,0.0256,0.0219,0.0043]) - f_ANSI = interp1d(f,BIF) - ANSIs= f_ANSI(fcenter); - return fcenter,ANSIs - - -def NCM(clean_speech,processed_speech,fs): - - x= clean_speech # clean signal - y= processed_speech # noisy signal - F_SIGNAL = fs - - F_ENVELOPE = 32 # limits modulations to 0 Ly: - x = x[0:Ly] - if Ly > Lx: - y = y[0:Lx] - - Lx = len(x); - Ly = len(y); - - X_BANDS = np.zeros((Lx,M_CHANNELS)) - Y_BANDS = np.zeros((Lx,M_CHANNELS)) - - # DESIGN BANDPASS FILTERS - for a in range(M_CHANNELS): - B_bp,A_bp = butter( 4 , np.array([BAND[a],BAND[a+1]])*(2/F_SIGNAL),btype='bandpass') - X_BANDS[:,a] = lfilter( B_bp , A_bp , x ) - Y_BANDS[:,a] = lfilter( B_bp , A_bp , y ) - - # CALCULATE HILBERT ENVELOPES, and resample at F_ENVELOPE Hz - analytic_x = hilbert( X_BANDS,axis=0); - X = np.abs( analytic_x ); - X = resample( X , round(len(x)/F_SIGNAL*F_ENVELOPE)); - - analytic_y = hilbert( Y_BANDS,axis=0); - Y = np.abs( analytic_y ); - Y = resample( Y , round(len(x)/F_SIGNAL*F_ENVELOPE)); - - ## ---compute weights based on clean signal's rms envelopes----- - # - Ldx, pp=X.shape - p=3 # power exponent - see Eq. 12 - - ro2 = np.zeros((M_CHANNELS,)) - asnr = np.zeros((M_CHANNELS,)) - TI = np.zeros((M_CHANNELS,)) - - for k in range(M_CHANNELS): - x_tmp= X[ :, k] - y_tmp= Y[ :, k] - lambda_x= np.linalg.norm( x_tmp- np.mean( x_tmp))**2 - lambda_y= np.linalg.norm( y_tmp- np.mean( y_tmp))**2 - lambda_xy= np.sum( (x_tmp- np.mean( x_tmp))*(y_tmp- np.mean( y_tmp))) - ro2[k]= (lambda_xy**2)/ (lambda_x*lambda_y) - asnr[k]= 10*np.log10( (ro2[k]+ 1e-20)/ (1- ro2[k]+ 1e-20)); # Eq.9 in [1] - - if asnr[k]< -15: - asnr[k]= -15 - elif asnr[k]> 15: - asnr[k]= 15 - - TI[k]= (asnr[k]+ 15)/ 30 # Eq.10 in [1] - - ncm_val= WEIGHT.dot(TI)/np.sum(WEIGHT) # Eq.11 - return ncm_val \ No newline at end of file + return Csig,Cbak,Covl \ No newline at end of file From d2a3c9fe239769604b0f19d3230e237445d52b01 Mon Sep 17 00:00:00 2001 From: schmiph2 Date: Wed, 17 Jun 2020 18:14:41 +0300 Subject: [PATCH 38/91] restructured project and rev measured added --- pysepm/intelligibilityMeasures.py | 0 pysepm/qualityMeasures.py | 0 pysepm/reverberationMeasures.py | 34 +++++++++++++++++++++++++++++++ 3 files changed, 34 insertions(+) mode change 100644 => 100755 pysepm/intelligibilityMeasures.py mode change 100644 => 100755 pysepm/qualityMeasures.py create mode 100755 pysepm/reverberationMeasures.py diff --git a/pysepm/intelligibilityMeasures.py b/pysepm/intelligibilityMeasures.py old mode 100644 new mode 100755 diff --git a/pysepm/qualityMeasures.py b/pysepm/qualityMeasures.py old mode 100644 new mode 100755 diff --git a/pysepm/reverberationMeasures.py b/pysepm/reverberationMeasures.py new file mode 100755 index 0000000..010ea0e --- /dev/null +++ b/pysepm/reverberationMeasures.py @@ -0,0 +1,34 @@ +from scipy.signal import resample +import srmrpy #https://github.com/jfsantos/SRMRpy + +from .qualityMeasures import SNRseg + +def srr_seg(clean_speech, processed_speech,fs): + return SNRseg(clean_speech, processed_speech,fs) + + +def srmr(speech,fs, n_cochlear_filters=23, low_freq=125, min_cf=4, max_cf=128, fast=True, norm=False): + if fs == 8000: + return srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + + elif fs == 16000: + return srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + + else: + numSamples=round(len(speech)/fs*16000) + fs = 16000 + return srmr(resample(speech, numSamples), fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + + + +def rdt(clean_speech, processed_speech,fs): + pass + + +def bsd(clean_speech, processed_speech,fs): + pass + + + + + From 1b4db91be243552a4d4834ee04b471e66062eead Mon Sep 17 00:00:00 2001 From: schmiph2 Date: Wed, 17 Jun 2020 18:15:42 +0300 Subject: [PATCH 39/91] init commit --- pysepm/util.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100755 pysepm/util.py diff --git a/pysepm/util.py b/pysepm/util.py new file mode 100755 index 0000000..2cc91fd --- /dev/null +++ b/pysepm/util.py @@ -0,0 +1,12 @@ +import numpy as np + +def extract_overlapped_windows(x,nperseg,noverlap,window=None): + # source: https://github.com/scipy/scipy/blob/v1.2.1/scipy/signal/spectral.py + step = nperseg - noverlap + shape = x.shape[:-1]+((x.shape[-1]-noverlap)//step, nperseg) + strides = x.strides[:-1]+(step*x.strides[-1], x.strides[-1]) + result = np.lib.stride_tricks.as_strided(x, shape=shape, + strides=strides) + if window is not None: + result = window * result + return result From 21dc326503b778fb6d4b678ef49b7136fd1bcdf5 Mon Sep 17 00:00:00 2001 From: schmiph2 Date: Wed, 17 Jun 2020 18:16:32 +0300 Subject: [PATCH 40/91] required packages added --- setup.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 415acdc..1d97fb2 100644 --- a/setup.py +++ b/setup.py @@ -1,5 +1,4 @@ -from setuptools import setup -from setuptools import find_packages +from setuptools import setup,find_packages setup( name='pysepm', @@ -9,6 +8,7 @@ author_email='scdp@zhaw.ch', url='https://github.zhaw.ch/scdp/py-sepm', license='MIT', + setup_requires = ['cython','wheel','numpy'], install_requires=[ 'cython', 'wheel', @@ -16,6 +16,8 @@ 'scipy', 'pystoi', 'pesq', + 'Gammatone @ https://github.com/detly/gammatone/archive/master.zip#egg=Gammatone', + 'SRMRpy @ https://github.com/jfsantos/SRMRpy/archive/master.zip#egg=srmr', ], classifiers=[ 'Development Status :: 4 - Beta', From 850fb7f0802de8ff7c020cea1673bc19f6d1406b Mon Sep 17 00:00:00 2001 From: schmiph2 Date: Wed, 17 Jun 2020 18:17:30 +0300 Subject: [PATCH 41/91] adapted to new structure --- pysepm/__init__.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) mode change 100644 => 100755 pysepm/__init__.py diff --git a/pysepm/__init__.py b/pysepm/__init__.py old mode 100644 new mode 100755 index 9672c8e..12fce64 --- a/pysepm/__init__.py +++ b/pysepm/__init__.py @@ -1,5 +1,8 @@ __version__ = '0.1' -from .speechEnhPerfMetrics import fwSNRseg,SNRseg,llr,wss,composite,pesq,stoi +from .qualityMeasures import fwSNRseg,SNRseg,llr,wss,composite,pesq +from .intelligibilityMeasures import stoi,csii,ncm +from .reverberationMeasures import srr_seg,rdt,bsd,srmr + From f8a298a734b7b642ba9e235c744b95c10ab69871 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Thu, 18 Jun 2020 16:20:23 +0200 Subject: [PATCH 42/91] setup script adapted to install package with pip install -e . --- setup.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/setup.py b/setup.py index 1d97fb2..bd66d55 100644 --- a/setup.py +++ b/setup.py @@ -6,18 +6,15 @@ description='Computes Objective Quality measures', author='Philipp Schmid', author_email='scdp@zhaw.ch', - url='https://github.zhaw.ch/scdp/py-sepm', + url='https://github.zhaw.ch/scdp/pysepm', license='MIT', - setup_requires = ['cython','wheel','numpy'], + setup_requires = ['Gammatone @ https://github.com/detly/gammatone/archive/master.zip#egg=Gammatone',], install_requires=[ - 'cython', - 'wheel', - 'numpy', + 'numpy', 'scipy', 'pystoi', - 'pesq', - 'Gammatone @ https://github.com/detly/gammatone/archive/master.zip#egg=Gammatone', - 'SRMRpy @ https://github.com/jfsantos/SRMRpy/archive/master.zip#egg=srmr', + 'pesq @ https://github.com/ludlows/python-pesq/archive/master.zip#egg=pesq', + 'SRMRpy @ https://github.com/schmiph2/SRMRpy/archive/master.zip#egg=SRMRpy', ], classifiers=[ 'Development Status :: 4 - Beta', From 34222d66db4badfdb3fa1bb45d66a91b537bddb7 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Thu, 18 Jun 2020 16:40:14 +0200 Subject: [PATCH 43/91] gammatone is not needed anymore as it is included in srmrpy, switched back to original dist of srmrpy due to merged PR --- setup.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/setup.py b/setup.py index bd66d55..2552b2d 100644 --- a/setup.py +++ b/setup.py @@ -8,13 +8,12 @@ author_email='scdp@zhaw.ch', url='https://github.zhaw.ch/scdp/pysepm', license='MIT', - setup_requires = ['Gammatone @ https://github.com/detly/gammatone/archive/master.zip#egg=Gammatone',], install_requires=[ 'numpy', 'scipy', 'pystoi', 'pesq @ https://github.com/ludlows/python-pesq/archive/master.zip#egg=pesq', - 'SRMRpy @ https://github.com/schmiph2/SRMRpy/archive/master.zip#egg=SRMRpy', + 'SRMRpy @ https://github.com/jfsantos/SRMRpy/archive/master.zip#egg=SRMRpy', ], classifiers=[ 'Development Status :: 4 - Beta', From ba360e272b2f657073d2e28a3600f4cb33b06699 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 18 Jun 2020 16:52:42 +0200 Subject: [PATCH 44/91] Update README.md --- README.md | 14 +------------- 1 file changed, 1 insertion(+), 13 deletions(-) diff --git a/README.md b/README.md index 42d60c9..79518a8 100644 --- a/README.md +++ b/README.md @@ -31,24 +31,12 @@ Python implementation of objective quality and intelligibilty measures mentioned + Speech to reverberation modulation ratio (SRMR) # Install with pip -The setup.py is not finished yet. You have to install the dependencies by hand. - -First install Cython (Required for python-pesq): -``` -pip3 install Cython -``` -Then install python-pesq and pystoi: -``` -pip3 install https://github.com/schmiph2/python-pesq/archive/master.zip -pip3 install https://github.com/schmiph2/pystoi/archive/master.zip -``` -Finally install pysepm: +Install pysepm: ``` pip3 install https://github.com/schmiph2/pysepm/archive/master.zip ``` # TODO: -+ Finish setup script + systematic tests for all measures + change resample method used (e.g. in NCM) to fit matlab implementation From 77511d6a579b0879fec12515a75838f47816cff9 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 18 Jun 2020 17:04:26 +0200 Subject: [PATCH 45/91] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 79518a8..60e329d 100644 --- a/README.md +++ b/README.md @@ -18,15 +18,15 @@ Python implementation of objective quality and intelligibilty measures mentioned + Weighted Spectral Slope (WSS) + Perceptual Evaluation of Speech Quality (PESQ) + Composite Objective Speech Quality (composite) ++ Cepstrum Distance Objective Speech Quality Measure (CD) ## Speech Intelligibility Measures + Short-time objective intelligibility (STOI) + Coherence and speech intelligibility index (CSII) + Normalized-covariance measure (NCM) -## Dereverberation Measures (TOD0) +## Dereverberation Measures (TODO) + Signal to reverberant ratio (SRR) -+ Reverberation decay tail (RDT) + Bark spectral distortion (BSD) + Speech to reverberation modulation ratio (SRMR) From fb07da495747ccc934ff0302694465b5baa46453 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Fri, 19 Jun 2020 15:07:23 +0200 Subject: [PATCH 46/91] cepstrum metric added --- pysepm/qualityMeasures.py | 88 ++++++++++++++++++++++++++++++++------- 1 file changed, 73 insertions(+), 15 deletions(-) diff --git a/pysepm/qualityMeasures.py b/pysepm/qualityMeasures.py index a9a51f9..162f040 100755 --- a/pysepm/qualityMeasures.py +++ b/pysepm/qualityMeasures.py @@ -1,6 +1,6 @@ from scipy.signal import stft,get_window,correlate,resample from scipy.linalg import solve_toeplitz,toeplitz -from pypesq import pypesq # https://github.com/ludlows/python-pesq +import pesq as pypesq # https://github.com/ludlows/python-pesq import numpy as np from .util import extract_overlapped_windows @@ -52,7 +52,7 @@ def fwSNRseg(cleanSig, enhancedSig, fs, frameLen=0.03, overlap=0.75): cent_freq[6] = 470.000; bandwidth[6] = 70.0000; cent_freq[7] = 540.000; bandwidth[7] = 77.3724; cent_freq[8] = 617.372; bandwidth[8] = 86.0056; - cent_freq[9] = 703.378; bandwidth[9] = 95.3398; + cent_freq[9] = 703.378; bandwidth[9] = 95.3398; cent_freq[10] = 798.717; bandwidth[10] = 105.411; cent_freq[11] = 904.128; bandwidth[11] = 116.256; cent_freq[12] = 1020.38; bandwidth[12] = 127.914; @@ -131,10 +131,9 @@ def lpcoeff(speech_frame, model_order): lpparams=np.ones((model_order+1)) lpparams[1:]=solve_toeplitz(R[0:-1],-R[1:]) - return(lpparams,R) -def llr(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): +def llr(clean_speech, processed_speech, fs, used_for_composite=False, frameLen=0.03, overlap=0.75): eps=np.finfo(np.float64).eps alpha = 0.95 winlength = round(frameLen*fs) #window length in samples @@ -162,7 +161,8 @@ def llr(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): frac=numerators/denominators frac[frac<=0]=1000 distortion = np.log(frac) - #distortion[distortion>2]=2 # this line is not in composite measure matlab implementation of loizou + if not used_for_composite: + distortion[distortion>2]=2 # this line is not in composite measure matlab implementation of loizou distortion = np.sort(distortion) distortion = distortion[:int(round(len(distortion)*alpha))] return np.mean(distortion) @@ -308,30 +308,32 @@ def wss(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): def pesq(clean_speech, processed_speech, fs): if fs == 8000: - pesq_mos = pypesq(fs,clean_speech, processed_speech, 'nb') - pesq_mos = 46607/14945 - (2000*np.log(1/(pesq_mos/4 - 999/4000) - 1))/2989 #remap to raw pesq score + mos_lqo = pypesq.pesq(fs,clean_speech, processed_speech, 'nb') + pesq_mos = 46607/14945 - (2000*np.log(1/(mos_lqo/4 - 999/4000) - 1))/2989 #remap to raw pesq score elif fs == 16000: - pesq_mos = pypesq(fs,clean_speech, processed_speech, 'wb') + mos_lqo = pypesq.pesq(fs,clean_speech, processed_speech, 'wb') + pesq_mos = 46607/14945 - (2000*np.log(1/(mos_lqo/4 - 999/4000) - 1))/2989 #remap to raw pesq score elif fs >= 16000: numSamples=round(len(clean_speech)/fs*16000) fs = 16000 - pesq_mos = pypesq(fs,resample(clean_speech, numSamples), resample(processed_speech, numSamples), 'wb') + mos_lqo = pypesq.pesq(fs,resample(clean_speech, numSamples), resample(processed_speech, numSamples), 'wb') + pesq_mos = 46607/14945 - (2000*np.log(1/(mos_lqo/4 - 999/4000) - 1))/2989 #remap to raw pesq score else: numSamples=round(len(clean_speech)/fs*8000) fs = 8000 - pesq_mos = pypesq(fs,resample(clean_speech, numSamples), resample(processed_speech, numSamples), 'nb') - pesq_mos = 46607/14945 - (2000*np.log(1/(pesq_mos/4 - 999/4000) - 1))/2989 #remap to raw pesq score + mos_lqo = pypesq.pesq(fs,resample(clean_speech, numSamples), resample(processed_speech, numSamples), 'nb') + pesq_mos = 46607/14945 - (2000*np.log(1/(mos_lqo/4 - 999/4000) - 1))/2989 #remap to raw pesq score - return pesq_mos + return mos_lqo,pesq_mos def composite(clean_speech, processed_speech, fs): wss_dist=wss(clean_speech, processed_speech, fs) - llr_mean=llr(clean_speech, processed_speech, fs) + llr_mean=llr(clean_speech, processed_speech, fs,used_for_composite=True) segSNR=SNRseg(clean_speech, processed_speech, fs) - pesq_mos = pesq(clean_speech, processed_speech,fs) + mos_lqo,pesq_mos = pesq(clean_speech, processed_speech,fs) Csig = 3.093 - 1.029*llr_mean + 0.603*pesq_mos-0.009*wss_dist Csig = np.max((1,Csig)) @@ -342,4 +344,60 @@ def composite(clean_speech, processed_speech, fs): Covl = 1.594 + 0.805*pesq_mos - 0.512*llr_mean - 0.007*wss_dist Covl = np.max((1, Covl)) Covl = np.min((5, Covl)) # limit values to [1, 5] - return Csig,Cbak,Covl \ No newline at end of file + return Csig,Cbak,Covl + +def lpc2cep(a): + # + # converts prediction to cepstrum coefficients + # + # Author: Philipos C. Loizou + + M=len(a); + cep=np.zeros((M-1,)); + + cep[0]=-a[1] + + for k in range(2,M): + ix=np.arange(1,k) + vec1=cep[ix-1]*a[k-1:0:-1]*(ix) + cep[k-1]=-(a[k]+np.sum(vec1)/k); + return cep + + +def cepstrum_distance(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): + + + clean_length = len(clean_speech) + processed_length = len(processed_speech) + + winlength = round(frameLen*fs) #window length in samples + skiprate = int(np.floor((1-overlap)*frameLen*fs)) #window skip in samples + + if fs<10000: + P = 10 # LPC Analysis Order + else: + P=16; # this could vary depending on sampling frequency. + + C=10*np.sqrt(2)/np.log(10) + + numFrames = int(clean_length/skiprate-(winlength/skiprate)); # number of frames + + hannWin=0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) + clean_speech_framed=extract_overlapped_windows(clean_speech[0:int(numFrames)*skiprate+int(winlength-skiprate)],winlength,winlength-skiprate,hannWin) + processed_speech_framed=extract_overlapped_windows(processed_speech[0:int(numFrames)*skiprate+int(winlength-skiprate)],winlength,winlength-skiprate,hannWin) + distortion = np.zeros((numFrames,)) + + for ii in range(numFrames): + A_clean,R_clean=lpcoeff(clean_speech_framed[ii,:],P) + A_proc,R_proc=lpcoeff(processed_speech_framed[ii,:],P) + + C_clean=lpc2cep(A_clean) + C_processed=lpc2cep(A_proc) + distortion[ii] = min((10,C*np.linalg.norm(C_clean-C_processed))) + + IS_dist = distortion + alpha=0.95 + IS_len= round( len( IS_dist)* alpha) + IS = np.sort(IS_dist) + cep_mean= np.mean( IS[ 0: IS_len]) + return cep_mean \ No newline at end of file From 7a9aa67cdcb290b7e0c715c23e16c461be96ccfc Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Fri, 19 Jun 2020 15:07:54 +0200 Subject: [PATCH 47/91] new pystoi import --- pysepm/intelligibilityMeasures.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pysepm/intelligibilityMeasures.py b/pysepm/intelligibilityMeasures.py index 7752148..090cdcd 100755 --- a/pysepm/intelligibilityMeasures.py +++ b/pysepm/intelligibilityMeasures.py @@ -5,7 +5,7 @@ from .util import extract_overlapped_windows -stoi = pystoi.stoi.stoi +stoi = pystoi.stoi def fwseg_noise(clean_speech, processed_speech,fs,frameLen=0.03, overlap=0.75): From a734d0f8be2802e2a3f6e3534a638c07f70174f1 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Fri, 19 Jun 2020 15:08:38 +0200 Subject: [PATCH 48/91] new metric added to init --- pysepm/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pysepm/__init__.py b/pysepm/__init__.py index 12fce64..fc64ef0 100755 --- a/pysepm/__init__.py +++ b/pysepm/__init__.py @@ -1,7 +1,7 @@ __version__ = '0.1' -from .qualityMeasures import fwSNRseg,SNRseg,llr,wss,composite,pesq +from .qualityMeasures import fwSNRseg,SNRseg,llr,wss,composite,pesq,cepstrum_distance from .intelligibilityMeasures import stoi,csii,ncm from .reverberationMeasures import srr_seg,rdt,bsd,srmr From 07811b531722280a7acb6db007a3af5835d79638 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Fri, 19 Jun 2020 15:09:10 +0200 Subject: [PATCH 49/91] tests with results of matlab reference implementations added --- tests/testPerfMetrics.ipynb | 594 +++++++++++++++++++++++++++++++----- 1 file changed, 523 insertions(+), 71 deletions(-) diff --git a/tests/testPerfMetrics.ipynb b/tests/testPerfMetrics.ipynb index 5db327d..083339a 100644 --- a/tests/testPerfMetrics.ipynb +++ b/tests/testPerfMetrics.ipynb @@ -6,12 +6,161 @@ "metadata": {}, "outputs": [], "source": [ - "from scipy.io import wavfile\n", + "from scipy.io import wavfile,loadmat\n", "from scipy.signal import stft,get_window, correlate\n", + "import librosa\n", "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sys\n", + "sys.path.append(\"../\") \n", + "\n", "import pysepm as pm #import fwSNRseg,SNRseg,llr,wss, composite,pesq,stoi" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "resultsOrig = loadmat('results.mat')\n", + "resultsOrig=resultsOrig['results']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "filePairs=[]\n", + "filePairs.append(['speech.wav','speech_bab_0dB.wav'])\n", + "filePairs.append(['sp04.wav','sp04_babble_sn10.wav'])\n", + "filePairs.append(['enhanced.wav','sp04_babble_sn10.wav'])\n", + "filePairs.append(['sp04.wav','enhanced.wav'])\n", + "filePairs.append(['S_03_01.wav','S_03_01_babble_sn0_klt.wav'])\n", + "filePairs.append(['cleanSample_valIdx12.wav','noisySample_valIdx12.wav'])\n", + "filePairs.append(['cleanSample_valIdx12.wav','enhancedSample_valIdx12.wav'])\n", + "filePairs.append(['cleanSample_valIdx356.wav','noisySample_valIdx356.wav'])\n", + "filePairs.append(['cleanSample_valIdx356.wav','enhancedSample_valIdx356.wav'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "results = np.zeros((len(filePairs),14))\n", + "for idx,filePair in enumerate(filePairs):\n", + " fs, cleanSig = wavfile.read(filePair[0])\n", + " fs, enhancedSig = wavfile.read(filePair[1])\n", + " if cleanSig.dtype=='int16':\n", + " cleanSig = cleanSig.astype('float')/abs(np.iinfo(cleanSig.dtype).min)\n", + " if enhancedSig.dtype=='int16':\n", + " enhancedSig = enhancedSig.astype('float')/abs(np.iinfo(enhancedSig.dtype).min)\n", + " \n", + " if cleanSig.shape!=enhancedSig.shape:\n", + " minLen=min((len(cleanSig),len(enhancedSig)))\n", + " cleanSig = cleanSig[0:minLen]\n", + " enhancedSig = enhancedSig[0:minLen]\n", + " cleanSig = cleanSig/np.max(np.abs(cleanSig))\n", + " enhancedSig = enhancedSig/np.max(np.abs(enhancedSig))\n", + " results[idx,0]=pm.fwSNRseg(cleanSig, enhancedSig, fs)\n", + " results[idx,1]=pm.SNRseg(cleanSig, enhancedSig, fs)\n", + " results[idx,2]=pm.llr(cleanSig, enhancedSig, fs)\n", + " results[idx,3]=pm.wss(cleanSig, enhancedSig, fs)\n", + " results[idx,4]=pm.cepstrum_distance(cleanSig, enhancedSig, fs)\n", + " results[idx,5]=pm.stoi(cleanSig, enhancedSig, fs)\n", + " CSIIh,CSIIm,CSIIl=pm.csii(cleanSig, enhancedSig, fs)\n", + " results[idx,6]=CSIIh\n", + " results[idx,7]=CSIIm\n", + " results[idx,8]=CSIIl\n", + " \n", + " if (fs!=16000) | (fs!=8000):\n", + " cleanSig=librosa.core.resample(cleanSig, fs,16000)\n", + " enhancedSig=librosa.core.resample(enhancedSig,fs,16000) \n", + " fs = 16000 \n", + " mos_lqo, pesq_mos = pm.pesq(cleanSig, enhancedSig,fs) \n", + " results[idx,9] = mos_lqo\n", + " Csig,Cbak,Covl=pm.composite(cleanSig, enhancedSig, fs)\n", + " results[idx,10]=Csig\n", + " results[idx,11]=Cbak\n", + " results[idx,12]=Covl\n", + " results[idx,13]=pm.ncm(cleanSig, enhancedSig, fs)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.00557757e-07, 5.82305131e-10, -1.52973926e-09,\n", + " 9.21022207e-09, -1.52513226e-09, 4.18685399e-05,\n", + " -1.42606629e-09, -7.68933070e-10, 6.42985470e-09,\n", + " -5.64410331e-06, -1.40880761e-01, -1.64403493e-01,\n", + " -2.67517220e-01, -5.62490318e-03],\n", + " [-1.59987136e-06, 6.89547874e-09, 2.56695880e-09,\n", + " -1.61318294e-08, 4.53037052e-09, 1.37028861e-05,\n", + " -2.14645365e-09, 9.72270751e-11, -3.99623268e-09,\n", + " -2.81971157e-02, 6.62283926e-02, 7.07303852e-02,\n", + " 1.11422091e-01, -3.51837880e-02],\n", + " [-2.89026689e-08, 1.07582911e-09, 1.13685130e-08,\n", + " -4.47057912e-08, 1.33628041e-08, -1.39590226e-04,\n", + " -1.46891603e-09, 6.72397331e-10, -6.64251355e-10,\n", + " 1.88176536e-03, -5.36503122e-03, 5.80227919e-02,\n", + " 5.48917065e-02, -2.09734119e-02],\n", + " [-6.23729375e-08, -1.72087206e-09, 1.22098149e-08,\n", + " -1.38945664e-08, 8.57458354e-09, -3.82818747e-05,\n", + " -1.72355789e-09, 2.28281874e-09, 2.23745188e-10,\n", + " 2.42903131e-02, 5.53501279e-02, 9.17225473e-02,\n", + " 1.30558018e-01, -2.73242243e-03],\n", + " [ 1.95809690e-07, -4.60415085e-09, -3.23102272e-08,\n", + " -6.22370895e-08, -2.15094159e-08, -3.40158840e-04,\n", + " -1.06141072e-08, 5.99642394e-09, -4.44663815e-08,\n", + " 1.49775826e-03, -1.31042382e-01, 3.21541380e-02,\n", + " -4.67349239e-02, -8.81628020e-03],\n", + " [-5.19009614e-15, -3.30978519e-16, -1.00689509e-12,\n", + " 4.76297147e-16, -3.46303801e-13, -4.32098332e-06,\n", + " -2.52930713e-10, 2.81795423e-08, -3.07701472e-08,\n", + " 2.27190991e-03, 2.07603352e-02, 2.59035275e-02,\n", + " 4.19050569e-02, -6.88522285e-04],\n", + " [ 5.64933006e-15, -8.90153085e-16, 3.18227315e-09,\n", + " 1.84146012e-15, 5.18962850e-11, 1.12080798e-05,\n", + " 2.15288715e-07, 5.04847697e-07, 1.99153996e-06,\n", + " -2.93397750e-04, -1.35709466e-01, -6.92200222e-02,\n", + " -1.28633105e-01, -5.18789582e-03],\n", + " [ 1.67628396e-15, -1.25202638e-15, -5.42118188e-13,\n", + " 7.60537654e-16, -3.99889630e-13, -2.47237404e-06,\n", + " -2.30973861e-07, 1.46921536e-07, 3.27272276e-08,\n", + " 2.02069067e-03, -7.42118666e-02, -1.04246783e-01,\n", + " -1.43331680e-01, -3.24183240e-04],\n", + " [-3.62677816e-16, 1.41554414e-15, 1.92248505e-10,\n", + " 7.03918921e-16, 3.79715942e-11, 3.30725671e-05,\n", + " 1.64786808e-07, 5.80454258e-07, 0.00000000e+00,\n", + " 1.25524566e-03, 0.00000000e+00, -1.20935074e-01,\n", + " 0.00000000e+00, -6.67466537e-03]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(results-resultsOrig)/(resultsOrig+1e-20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -29,7 +178,7 @@ { "data": { "text/plain": [ - "3.3554001065824384" + "3.355400106582439" ] }, "execution_count": 2, @@ -38,8 +187,8 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", "pm.fwSNRseg(cleanSig, enhancedSig, fs)# should be 3.3554\n" ] }, @@ -51,7 +200,7 @@ { "data": { "text/plain": [ - "10.093763940018947" + "10.093763940018944" ] }, "execution_count": 3, @@ -60,8 +209,8 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.fwSNRseg(cleanSig, enhancedSig, fs)# should be 10.0938\n" ] }, @@ -82,8 +231,8 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.fwSNRseg(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 12.5602\n" ] }, @@ -96,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -105,20 +254,20 @@ "-4.0386645840708395" ] }, - "execution_count": 5, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", "pm.SNRseg(cleanSig, enhancedSig, fs)# should be -4.0387\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -127,20 +276,20 @@ "0.959472693785315" ] }, - "execution_count": 6, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.SNRseg(cleanSig, enhancedSig, fs)# should be 0.9595\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -149,14 +298,14 @@ "5.281379775527017" ] }, - "execution_count": 7, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.SNRseg(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 5.2814\n" ] }, @@ -175,7 +324,7 @@ { "data": { "text/plain": [ - "0.9607521284185142" + "0.9607521284183373" ] }, "execution_count": 8, @@ -184,8 +333,8 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", "pm.llr(cleanSig, enhancedSig, fs)# should be 0.9593\n" ] }, @@ -197,7 +346,7 @@ { "data": { "text/plain": [ - "0.6399952859427331" + "0.6399952859426925" ] }, "execution_count": 9, @@ -206,8 +355,8 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.llr(cleanSig, enhancedSig, fs)# should be 0.6400\n" ] }, @@ -219,7 +368,7 @@ { "data": { "text/plain": [ - "0.22273917406045848" + "0.222739174060473" ] }, "execution_count": 10, @@ -228,8 +377,8 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.llr(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 0.2227" ] }, @@ -257,8 +406,8 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", "pm.wss(cleanSig, enhancedSig, fs)# should be 52.6579" ] }, @@ -270,7 +419,7 @@ { "data": { "text/plain": [ - "37.649017195834844" + "37.64901719583485" ] }, "execution_count": 12, @@ -279,8 +428,8 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.wss(cleanSig, enhancedSig, fs)# should be 37.6490" ] }, @@ -301,8 +450,8 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.wss(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 19.6127" ] }, @@ -315,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -324,20 +473,20 @@ "1.0832337141036987" ] }, - "execution_count": 20, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", "pm.pesq(cleanSig, enhancedSig, fs)# should be 1.0832" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -346,20 +495,20 @@ "2.463416557463939" ] }, - "execution_count": 21, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.pesq(cleanSig, enhancedSig, fs)# should be 2.4634" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -368,14 +517,14 @@ "2.7307331381405935" ] }, - "execution_count": 22, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.pesq(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 2.7293" ] }, @@ -388,70 +537,143 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(2.283655194486704, 1.5287447837866348, 1.6054929873447288)" + "(2.2836551944868853, 1.5287447837866348, 1.6054929873448194)" ] }, - "execution_count": 14, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", "pm.composite(cleanSig, enhancedSig, fs)# should be (2.2837,1.5287,1.6055)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(3.5810438801531697, 2.608416773805394, 2.985829621984948)" + "(3.581043880153211, 2.608416773805394, 2.9858296219849687)" ] }, - "execution_count": 15, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.composite(cleanSig, enhancedSig, fs)# should be (3.5810,2.6084,2.9858)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(4.333919599483947, 3.1347287982287013, 3.540909151423519)" + "(4.333919599483931, 3.1347287982287013, 3.5409091514235116)" ] }, - "execution_count": 16, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.composite(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be (4.3330,3.1340,3.5397)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cepstrum Distance Objective Speech Quality Measure" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.3889163971985266" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", + "pm.cepstrum_distance(cleanSig, enhancedSig, fs)# should be 6.388916397199216" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.26593320307941" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", + "pm.cepstrum_distance(cleanSig, enhancedSig, fs)# should be 4.265933203079426" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.3969895754520847" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", + "pm.cepstrum_distance(cleanSig, enhancedSig, fs)# should be 2.396989575452212" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -476,8 +698,8 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/speech_bab_0dB.wav\")\n", + "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", "pm.stoi(cleanSig, enhancedSig, fs)# should be 0.6739" ] }, @@ -498,8 +720,8 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.stoi(cleanSig, enhancedSig, fs)# should be 0.8934" ] }, @@ -520,11 +742,241 @@ } ], "source": [ - "fs, cleanSig = wavfile.read(\"./samples/enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./samples/sp04_babble_sn10.wav\")\n", + "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", "pm.stoi(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 0.8917" ] }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from pesq import pesq\n", + "from scipy.signal import stft,get_window,correlate,resample\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + " numSamples=round(len(cleanSig)/fs*16000)\n", + " fs = 16000\n", + " pesq_mos = pesq(fs,resample(cleanSig, numSamples), resample(enhancedSig, numSamples), 'wb')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NCM" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5328171511427755" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", + "pm.ncm(cleanSig, enhancedSig, fs)# should be " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9050747979588915" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", + "pm.ncm(cleanSig, enhancedSig, fs)# should be " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9633151692925036" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", + "pm.ncm(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CSII" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.5747598820639694, 0.3343790504514923, 0.02616214182320878)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", + "pm.csii(cleanSig, enhancedSig, fs)# should be " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9121855615693153, 0.622560057685167, 0.20949166842622877)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", + "pm.csii(cleanSig, enhancedSig, fs)# should be " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9761576893384923, 0.7825384403451644, 0.6281655003905069)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", + "pm.csii(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be " + ] + }, { "cell_type": "code", "execution_count": null, @@ -549,7 +1001,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, From bc0824fc16e1852451bd5d55239ab8e27d6b8db1 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Fri, 19 Jun 2020 15:11:02 +0200 Subject: [PATCH 50/91] init commit of results file of matlab reference implementations --- tests/results.mat | Bin 0 -> 1202 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/results.mat diff --git a/tests/results.mat b/tests/results.mat new file mode 100644 index 0000000000000000000000000000000000000000..f6bc5aca99bd8dc27c3337f1f31e988cb4b108e5 GIT binary patch literal 1202 zcmV;j1Wo%*K~zjZLLfCRFd$7qR4ry{Y-KDUP;6mzW^ZzBIv`L(S4mDbG%O%Pa%Ew3 zWn>_4ZaN@Fa%mt+b#5RrIUq4JIx;mnH8mhIFfuS8ARr(hARr(hARr(hARr(hARr(h zARr(hARr(hARr(hARr(hARr(B00000000000ZB~{0000g1ONbdoHdetFxz(=$A1xR z5;UX1JhUj$qaGIez*h75(x~WB(`EIrx^^hHmiCI$POmk+3H8*fpb@Kw8CE;G+Uah$ zCT)gEJTzBiTDl@(ng>ZdHzKpOd*8k8eed^Q??2vT48z=vF>LFm6S24cueQ9)q)o`1 z;5H#_l72elVrpi_f4v%yVX1=RkrxsZw6BD#!PBUyiC$SCc5p`vK_PqOo>U|e^y0q@ zq@aSu^^@acE~q7-&%G_*261Cv=v#cOP(&)r>_QaFOO=f#!FJja!> z!Jwa1Q023fDEUeX>Y%#nuDpIIGAG3ZQNr zqe3HxTY@~xxxjVE;t#9$F^`~->UlinSunEt?d5p7x~SAgo+KHOPYdy{)5MNX-atVy40sVU>J?S`$MK znFn63>M8~yCXg%$h2qydB+Xv#c)@$OS0 zP(wdpscfZ?Q7?YVNg0GQ9+tAXQ_V2txSMnCtOOF+Y*No$E#&;kOhO5Zu+wl1(fqU!g?IH$ zcF=+g!zN(YquaG(G7O_{V%ofm%pgE zx0HZr)$s*GiHp#ona{(5ap>(DHKa@*BB{iyur1d`WKwK|%92StTHOigJ#MyaY8{7s zOXLe#@>PiAAK`x*Vu0Wwwut&j2ld2wVU>{%4oqIBlP`}8b@1G1D8J3y$dkpy#0w?>j^E~^oSI=1+BpUdu%8lr?Ffo zVUj%Sy%DYeW4fs!Or?at!tsa8;YwI=iKv%dnE+MPQvIlh5 Date: Sat, 20 Jun 2020 16:09:04 +0200 Subject: [PATCH 51/91] prototype of BSD added --- pysepm/reverberationMeasures.py | 93 +++++++++++++++++++++++++++++---- 1 file changed, 84 insertions(+), 9 deletions(-) diff --git a/pysepm/reverberationMeasures.py b/pysepm/reverberationMeasures.py index 010ea0e..0ec43e4 100755 --- a/pysepm/reverberationMeasures.py +++ b/pysepm/reverberationMeasures.py @@ -1,6 +1,6 @@ -from scipy.signal import resample +from scipy.signal import resample,stft import srmrpy #https://github.com/jfsantos/SRMRpy - +import numpy as np from .qualityMeasures import SNRseg def srr_seg(clean_speech, processed_speech,fs): @@ -9,24 +9,99 @@ def srr_seg(clean_speech, processed_speech,fs): def srmr(speech,fs, n_cochlear_filters=23, low_freq=125, min_cf=4, max_cf=128, fast=True, norm=False): if fs == 8000: - return srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + return srmrpy.srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) elif fs == 16000: - return srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + return srmrpy.srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) else: numSamples=round(len(speech)/fs*16000) fs = 16000 - return srmr(resample(speech, numSamples), fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + return srmrpy.srmr(resample(speech, numSamples), fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + + +def hz_to_bark(freqs_hz): + freqs_hz = np.asanyarray([freqs_hz]) + barks = (26.81*freqs_hz)/(1960+freqs_hz)-0.53 + barks[barks<2]=barks[barks<2]+0.15*(2-barks[barks<2]) + barks[barks>20.1]=barks[barks>20.1]+0.22*(barks[barks>20.1]-20.1) + return np.squeeze(barks) + +def bark_to_hz(barks): + barks = barks.copy() + barks = np.asanyarray([barks]) + barks[barks<2]=(barks[barks<2]-0.3)/0.85 + barks[barks>20.1]=(barks[barks>20.1]+4.422)/1.22 + freqs_hz = 1960 * (barks+0.53)/(26.28-barks) + return np.squeeze(freqs_hz) + +def bark_frequencies(n_barks=128, fmin=0.0, fmax=11025.0): + # 'Center freqs' of bark bands - uniformly spaced between limits + min_bark = hz_to_bark(fmin) + max_bark = hz_to_bark(fmax) + + barks = np.linspace(min_bark, max_bark, n_barks) + + return bark_to_hz(barks) + +def barks(fs, n_fft, n_barks=128, fmin=0.0, fmax=None, norm='slaney', dtype=np.float32): + + if fmax is None: + fmax = float(fs) / 2 + + # Initialize the weights + n_barks = int(n_barks) + weights = np.zeros((n_barks, int(1 + n_fft // 2)), dtype=dtype) + # Center freqs of each FFT bin + fftfreqs = np.linspace(0,float(fs) / 2,int(1 + n_fft//2), endpoint=True) + + # 'Center freqs' of mel bands - uniformly spaced between limits + bark_f = bark_frequencies(n_barks + 2, fmin=fmin, fmax=fmax) + + fdiff = np.diff(bark_f) + ramps = np.subtract.outer(bark_f, fftfreqs) + + for i in range(n_barks): + # lower and upper slopes for all bins + lower = -ramps[i] / fdiff[i] + upper = ramps[i+2] / fdiff[i+1] + + # .. then intersect them with each other and zero + weights[i] = np.maximum(0, np.minimum(lower, upper)) + + if norm in (1, 'slaney'): + # Slaney-style bark is scaled to be approx constant energy per channel + enorm = 2.0 / (bark_f[2:n_barks+2] - bark_f[:n_barks]) + weights *= enorm[:, np.newaxis] + print('bark filter not tested') + return weights + +def bsd(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): -def rdt(clean_speech, processed_speech,fs): - pass + winlength = round(frameLen*fs) #window length in samples + skiprate = int(np.floor((1-overlap)*frameLen*fs)) #window skip in samples + max_freq = fs/2 #maximum bandwidth + n_fft = 2**np.ceil(np.log2(2*winlength)) + n_fftby2 = int(n_fft/2) + num_frames = len(clean_speech)/skiprate-(winlength/skiprate)# number of frames + print('include pre-emphasis') + + hannWin=0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) + f,t,Zxx=stft(clean_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=True, boundary=None, padded=False) + clean_power_spec=np.square(np.abs(Zxx)) + f,t,Zxx=stft(processed_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=True, boundary=None, padded=False) + enh_power_spec=np.square(np.abs(Zxx)) -def bsd(clean_speech, processed_speech,fs): - pass + bark_filt = barks(fs, n_fft, n_barks=32) + clean_power_spec_bark= np.dot(bark_filt,clean_power_spec) + enh_power_spec_bark= np.dot(bark_filt,enh_power_spec) + + + bsd = np.mean(np.sum(np.square(clean_power_spec_bark-enh_power_spec_bark),axis=0)/np.sum(np.square(clean_power_spec_bark),axis=0)) + return bsd,clean_power_spec_bark,enh_power_spec_bark From c9eec3884e9de24c13b3e944ee5eb5007cfb82ad Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Sat, 20 Jun 2020 16:09:34 +0200 Subject: [PATCH 52/91] rdt measure removed --- pysepm/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pysepm/__init__.py b/pysepm/__init__.py index fc64ef0..2fc724e 100755 --- a/pysepm/__init__.py +++ b/pysepm/__init__.py @@ -3,6 +3,6 @@ from .qualityMeasures import fwSNRseg,SNRseg,llr,wss,composite,pesq,cepstrum_distance from .intelligibilityMeasures import stoi,csii,ncm -from .reverberationMeasures import srr_seg,rdt,bsd,srmr +from .reverberationMeasures import srr_seg,bsd,srmr From 74c95f376829894c894c804309250a0405c2a464 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Sat, 20 Jun 2020 16:09:51 +0200 Subject: [PATCH 53/91] first tests of BSD --- tests/testPerfMetrics.ipynb | 283 ++++++++++++++++++++++++++++-------- 1 file changed, 220 insertions(+), 63 deletions(-) diff --git a/tests/testPerfMetrics.ipynb b/tests/testPerfMetrics.ipynb index 083339a..decdf77 100644 --- a/tests/testPerfMetrics.ipynb +++ b/tests/testPerfMetrics.ipynb @@ -17,10 +17,196 @@ "import pysepm as pm #import fwSNRseg,SNRseg,llr,wss, composite,pesq,stoi" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "include pre-emphasis\n", + "bark filter not tested\n", + "236903.55\n" + ] + } + ], + "source": [ + "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", + "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", + "bsd,clean,enh=pm.bsd(cleanSig, enhancedSig, fs)# should be 3.3554\n", + "print(bsd)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "nom=np.sum(np.square(clean-enh),axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "denom=np.sum(np.square(clean),axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "236903.55" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(nom/denom)" + ] + }, { "cell_type": "code", "execution_count": 2, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bark filter not tested\n" + ] + } + ], + "source": [ + "a = pm.reverberationMeasures.barks(22050,2048,24)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(a.T);" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0.17526951, 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0.35053903, 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " ...,\n", + " [0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ]], dtype=float32)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.T[0:100,:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 3)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "resultsOrig = loadmat('results.mat')\n", @@ -47,11 +233,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/srmrpy/hilbert.py:69: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", + " h = h[ind]\n" + ] + } + ], "source": [ - "results = np.zeros((len(filePairs),14))\n", + "results = np.zeros((len(filePairs),16))\n", "for idx,filePair in enumerate(filePairs):\n", " fs, cleanSig = wavfile.read(filePair[0])\n", " fs, enhancedSig = wavfile.read(filePair[1])\n", @@ -76,91 +271,53 @@ " results[idx,6]=CSIIh\n", " results[idx,7]=CSIIm\n", " results[idx,8]=CSIIl\n", + " results[idx,9]=CSIIl\n", " \n", " if (fs!=16000) | (fs!=8000):\n", " cleanSig=librosa.core.resample(cleanSig, fs,16000)\n", " enhancedSig=librosa.core.resample(enhancedSig,fs,16000) \n", " fs = 16000 \n", " mos_lqo, pesq_mos = pm.pesq(cleanSig, enhancedSig,fs) \n", - " results[idx,9] = mos_lqo\n", + " results[idx,10] = mos_lqo\n", " Csig,Cbak,Covl=pm.composite(cleanSig, enhancedSig, fs)\n", - " results[idx,10]=Csig\n", - " results[idx,11]=Cbak\n", - " results[idx,12]=Covl\n", - " results[idx,13]=pm.ncm(cleanSig, enhancedSig, fs)\n" + " results[idx,11]=Csig\n", + " results[idx,12]=Cbak\n", + " results[idx,13]=Covl\n", + " results[idx,14]=pm.ncm(cleanSig, enhancedSig, fs)\n", + " ratio, energy = pm.srmr(enhancedSig,fs, n_cochlear_filters=23, low_freq=125, min_cf=4, max_cf=128, fast=False)\n", + " results[idx,15]= ratio\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "relErr=(results-resultsOrig)/(resultsOrig+1e-20)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 1.00557757e-07, 5.82305131e-10, -1.52973926e-09,\n", - " 9.21022207e-09, -1.52513226e-09, 4.18685399e-05,\n", - " -1.42606629e-09, -7.68933070e-10, 6.42985470e-09,\n", - " -5.64410331e-06, -1.40880761e-01, -1.64403493e-01,\n", - " -2.67517220e-01, -5.62490318e-03],\n", - " [-1.59987136e-06, 6.89547874e-09, 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3.21541380e-02,\n", - " -4.67349239e-02, -8.81628020e-03],\n", - " [-5.19009614e-15, -3.30978519e-16, -1.00689509e-12,\n", - " 4.76297147e-16, -3.46303801e-13, -4.32098332e-06,\n", - " -2.52930713e-10, 2.81795423e-08, -3.07701472e-08,\n", - " 2.27190991e-03, 2.07603352e-02, 2.59035275e-02,\n", - " 4.19050569e-02, -6.88522285e-04],\n", - " [ 5.64933006e-15, -8.90153085e-16, 3.18227315e-09,\n", - " 1.84146012e-15, 5.18962850e-11, 1.12080798e-05,\n", - " 2.15288715e-07, 5.04847697e-07, 1.99153996e-06,\n", - " -2.93397750e-04, -1.35709466e-01, -6.92200222e-02,\n", - " -1.28633105e-01, -5.18789582e-03],\n", - " [ 1.67628396e-15, -1.25202638e-15, -5.42118188e-13,\n", - " 7.60537654e-16, -3.99889630e-13, -2.47237404e-06,\n", - " -2.30973861e-07, 1.46921536e-07, 3.27272276e-08,\n", - " 2.02069067e-03, -7.42118666e-02, -1.04246783e-01,\n", - " -1.43331680e-01, -3.24183240e-04],\n", - " [-3.62677816e-16, 1.41554414e-15, 1.92248505e-10,\n", - " 7.03918921e-16, 3.79715942e-11, 3.30725671e-05,\n", - " 1.64786808e-07, 5.80454258e-07, 0.00000000e+00,\n", - " 1.25524566e-03, 0.00000000e+00, -1.20935074e-01,\n", - " 0.00000000e+00, -6.67466537e-03]])" + "array([ 0.89866235, 9.87468543, 9.87757872, 8.07671193, -4.58756266,\n", + " 3.43626201, 3.00168894, 0.47358044, 2.37719669])" ] }, - "execution_count": 13, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "(results-resultsOrig)/(resultsOrig+1e-20)" + "relErr[:,-1]*100" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, From 9bd47d01722814426d7efcf56d235229e679e3e8 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Mon, 22 Jun 2020 08:42:10 +0200 Subject: [PATCH 54/91] Update README.md --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 60e329d..c16de8e 100644 --- a/README.md +++ b/README.md @@ -26,10 +26,8 @@ Python implementation of objective quality and intelligibilty measures mentioned + Normalized-covariance measure (NCM) ## Dereverberation Measures (TODO) -+ Signal to reverberant ratio (SRR) + Bark spectral distortion (BSD) -+ Speech to reverberation modulation ratio (SRMR) - ++ Scale-invariant signal to distortion ratio (SI-SDR) # Install with pip Install pysepm: ``` From 875a53d050be290c3bb8d6d9ba937e67d56e5784 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Mon, 22 Jun 2020 15:49:59 +0200 Subject: [PATCH 55/91] prototype of test added --- tests/test_metrics.py | 133 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 133 insertions(+) create mode 100644 tests/test_metrics.py diff --git a/tests/test_metrics.py b/tests/test_metrics.py new file mode 100644 index 0000000..e28dc50 --- /dev/null +++ b/tests/test_metrics.py @@ -0,0 +1,133 @@ +from scipy.io import wavfile,loadmat +import numpy as np +import sys +sys.path.append("../") +import pysepm as pm +import pytest +import numpy.testing +import librosa + +RTOL = 1e-5 +ATOL = 1e-4 + +resultsOrig = loadmat("results.mat") +resultsOrig=resultsOrig['results'] + +testScenarios=[] +pairCounter = 0 +testScenarios.append((['speech.wav','speech_bab_0dB.wav'],resultsOrig[pairCounter,:])) +pairCounter = pairCounter + 1 +testScenarios.append((['sp04.wav','sp04_babble_sn10.wav'],resultsOrig[pairCounter,:])) +pairCounter = pairCounter + 1 +testScenarios.append((['enhanced.wav','sp04_babble_sn10.wav'],resultsOrig[pairCounter,:])) +pairCounter = pairCounter + 1 +testScenarios.append((['sp04.wav','enhanced.wav'],resultsOrig[pairCounter,:])) +pairCounter = pairCounter + 1 +testScenarios.append((['S_03_01.wav','S_03_01_babble_sn0_klt.wav'],resultsOrig[pairCounter,:])) +pairCounter = pairCounter + 1 +testScenarios.append((['cleanSample_valIdx12.wav','noisySample_valIdx12.wav'],resultsOrig[pairCounter,:])) +pairCounter = pairCounter + 1 +testScenarios.append((['cleanSample_valIdx12.wav','enhancedSample_valIdx12.wav'],resultsOrig[pairCounter,:])) +pairCounter = pairCounter + 1 +testScenarios.append((['cleanSample_valIdx356.wav','noisySample_valIdx356.wav'],resultsOrig[pairCounter,:])) +pairCounter = pairCounter + 1 +testScenarios.append((['cleanSample_valIdx356.wav','enhancedSample_valIdx356.wav'],resultsOrig[pairCounter,:])) + + +def load_preprocess_filepair(filePair,resample=False,fs_targ=16000): + fs, cleanSig = wavfile.read('data/'+filePair[0]) + fs, enhancedSig = wavfile.read('data/'+filePair[1]) + if cleanSig.dtype=='int16': + cleanSig = cleanSig.astype('float')/abs(np.iinfo(cleanSig.dtype).min) + if enhancedSig.dtype=='int16': + enhancedSig = enhancedSig.astype('float')/abs(np.iinfo(enhancedSig.dtype).min) + + if cleanSig.shape!=enhancedSig.shape: + minLen=min((len(cleanSig),len(enhancedSig))) + cleanSig = cleanSig[0:minLen] + enhancedSig = enhancedSig[0:minLen] + cleanSig = cleanSig/np.max(np.abs(cleanSig)) + enhancedSig = enhancedSig/np.max(np.abs(enhancedSig)) + + if resample: + fs, cleanSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[0]) + fs, enhancedSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[1]) + + #cleanSig=librosa.core.resample(cleanSig, fs,fs_targ) + #enhancedSig=librosa.core.resample(enhancedSig,fs,fs_targ) + #fs = fs_targ + + return cleanSig,enhancedSig,fs + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) +def test_fwSNRseg(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) + numpy.testing.assert_allclose(pm.fwSNRseg(cleanSig, enhancedSig, fs), expected_vals[0], rtol=RTOL, atol=ATOL) + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) +def test_SNRseg(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) + numpy.testing.assert_allclose(pm.SNRseg(cleanSig, enhancedSig, fs), expected_vals[1], rtol=RTOL, atol=ATOL) + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) +def test_llr(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) + numpy.testing.assert_allclose(pm.llr(cleanSig, enhancedSig, fs), expected_vals[2], rtol=RTOL, atol=ATOL) + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) +def test_wss(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) + numpy.testing.assert_allclose(pm.wss(cleanSig, enhancedSig, fs), expected_vals[3], rtol=RTOL, atol=ATOL) + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) +def test_cepstrum_distance(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) + numpy.testing.assert_allclose(pm.cepstrum_distance(cleanSig, enhancedSig, fs), expected_vals[4], rtol=RTOL, atol=ATOL) + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +def test_stoi(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,10e3) + + numpy.testing.assert_allclose(pm.stoi(cleanSig, enhancedSig, fs), expected_vals[5], rtol=RTOL, atol=ATOL) + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +def test_csii(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) + CSIIh,CSIIm,CSIIl=pm.csii(cleanSig, enhancedSig, fs) + numpy.testing.assert_allclose(CSIIh, expected_vals[6], rtol=RTOL, atol=ATOL) + numpy.testing.assert_allclose(CSIIm, expected_vals[7], rtol=RTOL, atol=ATOL) + numpy.testing.assert_allclose(CSIIl, expected_vals[8], rtol=RTOL, atol=ATOL) + + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +def test_pesq(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) + mos_lqo, pesq_mos=pm.pesq(cleanSig, enhancedSig, fs) + numpy.testing.assert_allclose(mos_lqo, expected_vals[10], rtol=RTOL, atol=ATOL) + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +def test_composite(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) + Csig,Cbak,Covl=pm.composite(cleanSig, enhancedSig, fs) + numpy.testing.assert_allclose(Csig, expected_vals[11], rtol=RTOL, atol=ATOL) + numpy.testing.assert_allclose(Cbak, expected_vals[12], rtol=RTOL, atol=ATOL) + numpy.testing.assert_allclose(Covl, expected_vals[13], rtol=RTOL, atol=ATOL) + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +def test_ncm(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) + numpy.testing.assert_allclose(pm.ncm(cleanSig, enhancedSig, fs), expected_vals[14], rtol=RTOL, atol=ATOL) + + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +def test_srmr(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) + ratio,energy = pm.srmr(enhancedSig, fs) + numpy.testing.assert_allclose(ratio, expected_vals[15], rtol=RTOL, atol=ATOL) + + +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +def test_bsd(filePair,expected_vals): + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) + ratio,energy = pm.bsd(cleanSig, enhancedSig, fs) + numpy.testing.assert_allclose(ratio, np.inf, rtol=RTOL, atol=ATOL) From a7a88e26c1902e0799d23b1e321e51f9e87f8bca Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Tue, 23 Jun 2020 14:08:09 +0200 Subject: [PATCH 56/91] updated pesq calculations --- pysepm/qualityMeasures.py | 33 ++++++++++++++------------------- 1 file changed, 14 insertions(+), 19 deletions(-) diff --git a/pysepm/qualityMeasures.py b/pysepm/qualityMeasures.py index 162f040..8552801 100755 --- a/pysepm/qualityMeasures.py +++ b/pysepm/qualityMeasures.py @@ -309,39 +309,34 @@ def wss(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): def pesq(clean_speech, processed_speech, fs): if fs == 8000: mos_lqo = pypesq.pesq(fs,clean_speech, processed_speech, 'nb') - pesq_mos = 46607/14945 - (2000*np.log(1/(mos_lqo/4 - 999/4000) - 1))/2989 #remap to raw pesq score - + pesq_mos = 46607/14945 - (2000*np.log(1/(mos_lqo/4 - 999/4000) - 1))/2989#0.999 + ( 4.999-0.999 ) / ( 1+np.exp(-1.4945*pesq_mos+4.6607) ) elif fs == 16000: mos_lqo = pypesq.pesq(fs,clean_speech, processed_speech, 'wb') - pesq_mos = 46607/14945 - (2000*np.log(1/(mos_lqo/4 - 999/4000) - 1))/2989 #remap to raw pesq score - elif fs >= 16000: - numSamples=round(len(clean_speech)/fs*16000) - fs = 16000 - mos_lqo = pypesq.pesq(fs,resample(clean_speech, numSamples), resample(processed_speech, numSamples), 'wb') - pesq_mos = 46607/14945 - (2000*np.log(1/(mos_lqo/4 - 999/4000) - 1))/2989 #remap to raw pesq score + pesq_mos = np.NaN else: - numSamples=round(len(clean_speech)/fs*8000) - fs = 8000 - mos_lqo = pypesq.pesq(fs,resample(clean_speech, numSamples), resample(processed_speech, numSamples), 'nb') - pesq_mos = 46607/14945 - (2000*np.log(1/(mos_lqo/4 - 999/4000) - 1))/2989 #remap to raw pesq score - - return mos_lqo,pesq_mos - + raise ValueError('fs must be either 8 kHz or 16 kHz') + + return pesq_mos,mos_lqo def composite(clean_speech, processed_speech, fs): wss_dist=wss(clean_speech, processed_speech, fs) llr_mean=llr(clean_speech, processed_speech, fs,used_for_composite=True) segSNR=SNRseg(clean_speech, processed_speech, fs) - mos_lqo,pesq_mos = pesq(clean_speech, processed_speech,fs) + pesq_mos,mos_lqo = pesq(clean_speech, processed_speech,fs) + + if fs >= 16e3: + used_pesq_val = mos_lqo + else: + used_pesq_val = pesq_mos - Csig = 3.093 - 1.029*llr_mean + 0.603*pesq_mos-0.009*wss_dist + Csig = 3.093 - 1.029*llr_mean + 0.603*used_pesq_val-0.009*wss_dist Csig = np.max((1,Csig)) Csig = np.min((5, Csig)) # limit values to [1, 5] - Cbak = 1.634 + 0.478 *pesq_mos - 0.007*wss_dist + 0.063*segSNR + Cbak = 1.634 + 0.478 *used_pesq_val - 0.007*wss_dist + 0.063*segSNR Cbak = np.max((1, Cbak)) Cbak = np.min((5,Cbak)) # limit values to [1, 5] - Covl = 1.594 + 0.805*pesq_mos - 0.512*llr_mean - 0.007*wss_dist + Covl = 1.594 + 0.805*used_pesq_val - 0.512*llr_mean - 0.007*wss_dist Covl = np.max((1, Covl)) Covl = np.min((5, Covl)) # limit values to [1, 5] return Csig,Cbak,Covl From f0eb6790c6659a6857bb8ccd6006cd520b67ddaa Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Tue, 23 Jun 2020 14:09:29 +0200 Subject: [PATCH 57/91] removed implicit resampling in ncm metric --- pysepm/intelligibilityMeasures.py | 19 +++++-------------- 1 file changed, 5 insertions(+), 14 deletions(-) diff --git a/pysepm/intelligibilityMeasures.py b/pysepm/intelligibilityMeasures.py index 090cdcd..772b3d3 100755 --- a/pysepm/intelligibilityMeasures.py +++ b/pysepm/intelligibilityMeasures.py @@ -199,19 +199,9 @@ def get_ansis(BAND): def ncm(clean_speech,processed_speech,fs): if fs != 8000 and fs != 16000: - if fs >= 16000: - numSamples=round(len(clean_speech)/fs*16000) - clean_speech = resample(clean_speech, numSamples) - numSamples=round(len(processed_speech)/fs*16000) - processed_speech = resample(processed_speech, numSamples) - fs = 16000 - else: - numSamples=round(len(clean_speech)/fs*8000) - clean_speech = resample(clean_speech, numSamples) - numSamples=round(len(processed_speech)/fs*8000) - processed_speech = resample(processed_speech, numSamples) - fs = 8000 - + raise ValueError('fs must be either 8 kHz or 16 kHz') + + x= clean_speech # clean signal y= processed_speech # noisy signal @@ -249,6 +239,7 @@ def ncm(clean_speech,processed_speech,fs): X_BANDS[:,a] = lfilter( B_bp , A_bp , x ) Y_BANDS[:,a] = lfilter( B_bp , A_bp , y ) + gcd = np.gcd(F_SIGNAL, F_ENVELOPE) # CALCULATE HILBERT ENVELOPES, and resample at F_ENVELOPE Hz analytic_x = hilbert( X_BANDS,axis=0); X = np.abs( analytic_x ); @@ -284,4 +275,4 @@ def ncm(clean_speech,processed_speech,fs): TI[k]= (asnr[k]+ 15)/ 30 # Eq.10 in [1] ncm_val= WEIGHT.dot(TI)/np.sum(WEIGHT) # Eq.11 - return ncm_val \ No newline at end of file + return ncm_val From 49921dc72bdd068001d28244a386aad68599f141 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Tue, 23 Jun 2020 14:09:59 +0200 Subject: [PATCH 58/91] changed default proporties of srmr --- pysepm/reverberationMeasures.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pysepm/reverberationMeasures.py b/pysepm/reverberationMeasures.py index 0ec43e4..59e0bd9 100755 --- a/pysepm/reverberationMeasures.py +++ b/pysepm/reverberationMeasures.py @@ -7,7 +7,7 @@ def srr_seg(clean_speech, processed_speech,fs): return SNRseg(clean_speech, processed_speech,fs) -def srmr(speech,fs, n_cochlear_filters=23, low_freq=125, min_cf=4, max_cf=128, fast=True, norm=False): +def srmr(speech,fs, n_cochlear_filters=23, low_freq=125, min_cf=4, max_cf=128, fast=False, norm=False): if fs == 8000: return srmrpy.srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) From 051c6af37eb8add7d8b8819bf8d88fd6f20ffb3f Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Tue, 23 Jun 2020 14:10:39 +0200 Subject: [PATCH 59/91] prototype v2 of tets --- tests/test_metrics.py | 49 ++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 26 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index e28dc50..7550e3a 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -7,8 +7,8 @@ import numpy.testing import librosa -RTOL = 1e-5 -ATOL = 1e-4 +RTOL = 1e-3 +ATOL = 1e-3 resultsOrig = loadmat("results.mat") resultsOrig=resultsOrig['results'] @@ -37,21 +37,16 @@ def load_preprocess_filepair(filePair,resample=False,fs_targ=16000): fs, cleanSig = wavfile.read('data/'+filePair[0]) fs, enhancedSig = wavfile.read('data/'+filePair[1]) - if cleanSig.dtype=='int16': - cleanSig = cleanSig.astype('float')/abs(np.iinfo(cleanSig.dtype).min) - if enhancedSig.dtype=='int16': - enhancedSig = enhancedSig.astype('float')/abs(np.iinfo(enhancedSig.dtype).min) - - if cleanSig.shape!=enhancedSig.shape: - minLen=min((len(cleanSig),len(enhancedSig))) - cleanSig = cleanSig[0:minLen] - enhancedSig = enhancedSig[0:minLen] - cleanSig = cleanSig/np.max(np.abs(cleanSig)) - enhancedSig = enhancedSig/np.max(np.abs(enhancedSig)) - + if resample: - fs, cleanSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[0]) - fs, enhancedSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[1]) + if fs_targ == 16000 and fs !=8000: + fs, cleanSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[0]) + fs, enhancedSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[1]) + elif fs_targ == 16000 and fs == 8000: + pass + else: + fs, cleanSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[0]) + fs, enhancedSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[1]) #cleanSig=librosa.core.resample(cleanSig, fs,fs_targ) #enhancedSig=librosa.core.resample(enhancedSig,fs,fs_targ) @@ -90,7 +85,7 @@ def test_stoi(filePair,expected_vals): numpy.testing.assert_allclose(pm.stoi(cleanSig, enhancedSig, fs), expected_vals[5], rtol=RTOL, atol=ATOL) -@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_csii(filePair,expected_vals): cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) CSIIh,CSIIm,CSIIl=pm.csii(cleanSig, enhancedSig, fs) @@ -99,13 +94,13 @@ def test_csii(filePair,expected_vals): numpy.testing.assert_allclose(CSIIl, expected_vals[8], rtol=RTOL, atol=ATOL) -@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_pesq(filePair,expected_vals): cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) - mos_lqo, pesq_mos=pm.pesq(cleanSig, enhancedSig, fs) + pesq_mos,mos_lqo=pm.pesq(cleanSig, enhancedSig, fs) numpy.testing.assert_allclose(mos_lqo, expected_vals[10], rtol=RTOL, atol=ATOL) -@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_composite(filePair,expected_vals): cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) Csig,Cbak,Covl=pm.composite(cleanSig, enhancedSig, fs) @@ -113,21 +108,23 @@ def test_composite(filePair,expected_vals): numpy.testing.assert_allclose(Cbak, expected_vals[12], rtol=RTOL, atol=ATOL) numpy.testing.assert_allclose(Covl, expected_vals[13], rtol=RTOL, atol=ATOL) -@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_ncm(filePair,expected_vals): + rto = 5e-3 + atol = 2e-3 # higher tolerances due to differences between the used resampling method und the one of matlab cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) - numpy.testing.assert_allclose(pm.ncm(cleanSig, enhancedSig, fs), expected_vals[14], rtol=RTOL, atol=ATOL) + numpy.testing.assert_allclose(pm.ncm(cleanSig, enhancedSig, fs), expected_vals[14], rtol=rto, atol=atol) -@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_srmr(filePair,expected_vals): cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) ratio,energy = pm.srmr(enhancedSig, fs) numpy.testing.assert_allclose(ratio, expected_vals[15], rtol=RTOL, atol=ATOL) -@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_bsd(filePair,expected_vals): cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) - ratio,energy = pm.bsd(cleanSig, enhancedSig, fs) - numpy.testing.assert_allclose(ratio, np.inf, rtol=RTOL, atol=ATOL) + bsd = pm.bsd(cleanSig, enhancedSig, fs) + numpy.testing.assert_allclose(bsd, 1e20, rtol=RTOL, atol=ATOL) From fa9372ff19425a2f00bd7ef31e5f87490f6b1228 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Tue, 7 Jul 2020 18:21:20 +0200 Subject: [PATCH 60/91] updated resampling method to the same as matlab --- pysepm/intelligibilityMeasures.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pysepm/intelligibilityMeasures.py b/pysepm/intelligibilityMeasures.py index 772b3d3..f54fda9 100755 --- a/pysepm/intelligibilityMeasures.py +++ b/pysepm/intelligibilityMeasures.py @@ -3,7 +3,7 @@ import pystoi.stoi # https://github.com/mpariente/pystoi import numpy as np -from .util import extract_overlapped_windows +from .util import extract_overlapped_windows,resample_matlab_like stoi = pystoi.stoi @@ -243,12 +243,12 @@ def ncm(clean_speech,processed_speech,fs): # CALCULATE HILBERT ENVELOPES, and resample at F_ENVELOPE Hz analytic_x = hilbert( X_BANDS,axis=0); X = np.abs( analytic_x ); - X = resample( X , round(len(x)/F_SIGNAL*F_ENVELOPE)); - + #X = resample( X , round(len(x)/F_SIGNAL*F_ENVELOPE)); + X = resample_matlab_like(X,F_ENVELOPE,F_SIGNAL) analytic_y = hilbert( Y_BANDS,axis=0); Y = np.abs( analytic_y ); - Y = resample( Y , round(len(x)/F_SIGNAL*F_ENVELOPE)); - + #Y = resample( Y , round(len(x)/F_SIGNAL*F_ENVELOPE)); + Y = resample_matlab_like(Y,F_ENVELOPE,F_SIGNAL) ## ---compute weights based on clean signal's rms envelopes----- # Ldx, pp=X.shape From 3143ccc90936f893f8ddd79f2eca9e26cbffde34 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Tue, 7 Jul 2020 18:21:49 +0200 Subject: [PATCH 61/91] updated lpcoeff calculation to the same as original implementation --- pysepm/qualityMeasures.py | 51 ++++++++++++++++++++++++++++++++++++--- 1 file changed, 48 insertions(+), 3 deletions(-) diff --git a/pysepm/qualityMeasures.py b/pysepm/qualityMeasures.py index 8552801..6d3c0aa 100755 --- a/pysepm/qualityMeasures.py +++ b/pysepm/qualityMeasures.py @@ -118,18 +118,63 @@ def fwSNRseg(cleanSig, enhancedSig, fs, frameLen=0.03, overlap=0.75): return np.mean(distortion) +#def lpcoeff(speech_frame, model_order): +# # ---------------------------------------------------------- +# # (1) Compute Autocorrelation Lags +# # ---------------------------------------------------------- +# +# R=correlate(speech_frame,speech_frame) +# R=R[len(speech_frame)-1:len(speech_frame)+model_order] +# # ---------------------------------------------------------- +# # (2) Levinson-Durbin +# # ---------------------------------------------------------- +# lpparams=np.ones((model_order+1)) +# lpparams[1:]=solve_toeplitz(R[0:-1],-R[1:]) +# +# return(lpparams,R) + def lpcoeff(speech_frame, model_order): # ---------------------------------------------------------- # (1) Compute Autocorrelation Lags # ---------------------------------------------------------- - + winlength = max(speech_frame.shape) + #R = np.zeros((model_order+1,)) + #for k in range(model_order+1): + # if k==0: + # R[k]=np.sum(speech_frame[0:]*speech_frame[0:]) + # else: + # R[k]=np.sum(speech_frame[0:-k]*speech_frame[k:]) + # + R=correlate(speech_frame,speech_frame) R=R[len(speech_frame)-1:len(speech_frame)+model_order] # ---------------------------------------------------------- # (2) Levinson-Durbin # ---------------------------------------------------------- - lpparams=np.ones((model_order+1)) - lpparams[1:]=solve_toeplitz(R[0:-1],-R[1:]) + a = np.ones((model_order,)) + a_past = np.ones((model_order,)) + rcoeff = np.zeros((model_order,)) + E = np.zeros((model_order+1,)) + + E[0]=R[0] + + for i in range(0,model_order): + a_past[0:i] = a[0:i] + + sum_term = sum(a_past[0:i]*R[i:0:-1]) + rcoeff[i]=(R[i+1] - sum_term) / E[i] + a[i]=rcoeff[i] + if i==0: + a[0:i] = a_past[0:i] - rcoeff[i]*np.array([]) + else: + a[0:i] = a_past[0:i] - rcoeff[i]*a_past[i-1::-1] + + E[i+1]=(1-rcoeff[i]*rcoeff[i])*E[i] + + acorr = R; + refcoeff = rcoeff; + lpparams = np.ones((model_order+1,)) + lpparams[1:] = -a return(lpparams,R) From a05a59b0dc4aa386bb51cf275711df996197f420 Mon Sep 17 00:00:00 2001 From: Schmid Philipp Matthias Date: Tue, 7 Jul 2020 18:22:12 +0200 Subject: [PATCH 62/91] added matlab like resampling --- pysepm/util.py | 43 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) diff --git a/pysepm/util.py b/pysepm/util.py index 2cc91fd..608ba20 100755 --- a/pysepm/util.py +++ b/pysepm/util.py @@ -1,4 +1,6 @@ import numpy as np +from scipy.signal import firls,kaiser,upfirdn +from fractions import Fraction def extract_overlapped_windows(x,nperseg,noverlap,window=None): # source: https://github.com/scipy/scipy/blob/v1.2.1/scipy/signal/spectral.py @@ -10,3 +12,44 @@ def extract_overlapped_windows(x,nperseg,noverlap,window=None): if window is not None: result = window * result return result + +def resample_matlab_like(x_orig,p,q): + if len(x_orig.shape)>2: + raise ValueError('x must be a vector or 2d matrix') + + if x_orig.shape[0] Date: Tue, 7 Jul 2020 18:23:38 +0200 Subject: [PATCH 63/91] updated test files and test tolerances --- tests/test_metrics.py | 125 +++++++++++++++++++++++++++++++----------- 1 file changed, 93 insertions(+), 32 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 7550e3a..0da517a 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -7,31 +7,44 @@ import numpy.testing import librosa -RTOL = 1e-3 -ATOL = 1e-3 +RTOL = 1e-12 +ATOL = 0 resultsOrig = loadmat("results.mat") resultsOrig=resultsOrig['results'] + + +freqs = [44100,22050,16000,8000] +pairs = [['speech','noisySpeech'],['speech','processed']] testScenarios=[] pairCounter = 0 -testScenarios.append((['speech.wav','speech_bab_0dB.wav'],resultsOrig[pairCounter,:])) -pairCounter = pairCounter + 1 -testScenarios.append((['sp04.wav','sp04_babble_sn10.wav'],resultsOrig[pairCounter,:])) -pairCounter = pairCounter + 1 -testScenarios.append((['enhanced.wav','sp04_babble_sn10.wav'],resultsOrig[pairCounter,:])) -pairCounter = pairCounter + 1 -testScenarios.append((['sp04.wav','enhanced.wav'],resultsOrig[pairCounter,:])) -pairCounter = pairCounter + 1 -testScenarios.append((['S_03_01.wav','S_03_01_babble_sn0_klt.wav'],resultsOrig[pairCounter,:])) -pairCounter = pairCounter + 1 -testScenarios.append((['cleanSample_valIdx12.wav','noisySample_valIdx12.wav'],resultsOrig[pairCounter,:])) -pairCounter = pairCounter + 1 -testScenarios.append((['cleanSample_valIdx12.wav','enhancedSample_valIdx12.wav'],resultsOrig[pairCounter,:])) -pairCounter = pairCounter + 1 -testScenarios.append((['cleanSample_valIdx356.wav','noisySample_valIdx356.wav'],resultsOrig[pairCounter,:])) -pairCounter = pairCounter + 1 -testScenarios.append((['cleanSample_valIdx356.wav','enhancedSample_valIdx356.wav'],resultsOrig[pairCounter,:])) +for ii in range(1,4): + for f in freqs: + for pair in pairs: + fileNameClean="{}_{}_{}_Hz.wav".format(ii,pair[0],f) + fileNameNoisy="{}_{}_{}_Hz.wav".format(ii,pair[1],f) + testScenarios.append(([fileNameClean,fileNameNoisy],resultsOrig[pairCounter])) + pairCounter=pairCounter+1 + + +#testScenarios.append((['speech.wav','speech_bab_0dB.wav'],resultsOrig[pairCounter,:])) +#pairCounter = pairCounter + 1 +#testScenarios.append((['sp04.wav','sp04_babble_sn10.wav'],resultsOrig[pairCounter,:])) +#pairCounter = pairCounter + 1 +#testScenarios.append((['enhanced.wav','sp04_babble_sn10.wav'],resultsOrig[pairCounter,:])) +#pairCounter = pairCounter + 1 +#testScenarios.append((['sp04.wav','enhanced.wav'],resultsOrig[pairCounter,:])) +#pairCounter = pairCounter + 1 +#testScenarios.append((['S_03_01.wav','S_03_01_babble_sn0_klt.wav'],resultsOrig[pairCounter,:])) +#pairCounter = pairCounter + 1 +#testScenarios.append((['cleanSample_valIdx12.wav','noisySample_valIdx12.wav'],resultsOrig[pairCounter,:])) +#pairCounter = pairCounter + 1 +#testScenarios.append((['cleanSample_valIdx12.wav','enhancedSample_valIdx12.wav'],resultsOrig[pairCounter,:])) +#pairCounter = pairCounter + 1 +#testScenarios.append((['cleanSample_valIdx356.wav','noisySample_valIdx356.wav'],resultsOrig[pairCounter,:])) +#pairCounter = pairCounter + 1 +#testScenarios.append((['cleanSample_valIdx356.wav','enhancedSample_valIdx356.wav'],resultsOrig[pairCounter,:])) def load_preprocess_filepair(filePair,resample=False,fs_targ=16000): @@ -40,13 +53,32 @@ def load_preprocess_filepair(filePair,resample=False,fs_targ=16000): if resample: if fs_targ == 16000 and fs !=8000: - fs, cleanSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[0]) - fs, enhancedSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[1]) + strParts=filePair[0].split('_') + fileNameClean="{}_{}_{}_Hz.wav".format(strParts[0],strParts[1],16000) + fs, cleanSig = wavfile.read('data/'+fileNameClean) + + strParts=filePair[1].split('_') + fileNameNoisy="{}_{}_{}_Hz.wav".format(strParts[0],strParts[1],16000) + fs, enhancedSig = wavfile.read('data/'+fileNameNoisy) + + #fs, cleanSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[0]) + #fs, enhancedSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[1]) elif fs_targ == 16000 and fs == 8000: pass else: - fs, cleanSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[0]) - fs, enhancedSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[1]) + #strParts=filePair[0].split('_') + #fileNameClean="{}_{}_{}_Hz.wav".format(strParts[0],strParts[1],10000) + #fs, cleanSig = wavfile.read('data/'+fileNameClean) + + #strParts=filePair[1].split('_') + #fileNameNoisy="{}_{}_{}_Hz.wav".format(strParts[0],strParts[1],10000) + #fs, enhancedSig = wavfile.read('data/'+fileNameNoisy) + + cleanSig = librosa.core.resample(cleanSig, fs, fs_targ) + enhancedSig = librosa.core.resample(enhancedSig, fs, fs_targ) + fs = fs_targ + #fs, cleanSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[0]) + #fs, enhancedSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[1]) #cleanSig=librosa.core.resample(cleanSig, fs,fs_targ) #enhancedSig=librosa.core.resample(enhancedSig,fs,fs_targ) @@ -56,37 +88,58 @@ def load_preprocess_filepair(filePair,resample=False,fs_targ=16000): @pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_fwSNRseg(filePair,expected_vals): + RTOL = 1e-12 + ATOL = 0 + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) numpy.testing.assert_allclose(pm.fwSNRseg(cleanSig, enhancedSig, fs), expected_vals[0], rtol=RTOL, atol=ATOL) @pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_SNRseg(filePair,expected_vals): + RTOL = 1e-12 + ATOL = 0 + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) numpy.testing.assert_allclose(pm.SNRseg(cleanSig, enhancedSig, fs), expected_vals[1], rtol=RTOL, atol=ATOL) @pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_llr(filePair,expected_vals): + RTOL = 5e-9 + ATOL = 0 + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) numpy.testing.assert_allclose(pm.llr(cleanSig, enhancedSig, fs), expected_vals[2], rtol=RTOL, atol=ATOL) @pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_wss(filePair,expected_vals): + RTOL = 1e-12 + ATOL = 0 + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) numpy.testing.assert_allclose(pm.wss(cleanSig, enhancedSig, fs), expected_vals[3], rtol=RTOL, atol=ATOL) @pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_cepstrum_distance(filePair,expected_vals): + RTOL = 1e-8 + ATOL = 0 + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) numpy.testing.assert_allclose(pm.cepstrum_distance(cleanSig, enhancedSig, fs), expected_vals[4], rtol=RTOL, atol=ATOL) -@pytest.mark.parametrize('filePair,expected_vals', testScenarios) # needs 10 kHz samping rate! +@pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_stoi(filePair,expected_vals): + RTOL = 5e-3#5e-4 + ATOL = 0 + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,10e3) numpy.testing.assert_allclose(pm.stoi(cleanSig, enhancedSig, fs), expected_vals[5], rtol=RTOL, atol=ATOL) @pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_csii(filePair,expected_vals): + RTOL = 5e-4 + ATOL = 0 + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) CSIIh,CSIIm,CSIIl=pm.csii(cleanSig, enhancedSig, fs) numpy.testing.assert_allclose(CSIIh, expected_vals[6], rtol=RTOL, atol=ATOL) @@ -96,12 +149,18 @@ def test_csii(filePair,expected_vals): @pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_pesq(filePair,expected_vals): + RTOL = 5e-4 + ATOL = 0 + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) pesq_mos,mos_lqo=pm.pesq(cleanSig, enhancedSig, fs) numpy.testing.assert_allclose(mos_lqo, expected_vals[10], rtol=RTOL, atol=ATOL) @pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_composite(filePair,expected_vals): + RTOL = 5e-4 + ATOL = 0 + cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) Csig,Cbak,Covl=pm.composite(cleanSig, enhancedSig, fs) numpy.testing.assert_allclose(Csig, expected_vals[11], rtol=RTOL, atol=ATOL) @@ -110,21 +169,23 @@ def test_composite(filePair,expected_vals): @pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_ncm(filePair,expected_vals): - rto = 5e-3 - atol = 2e-3 # higher tolerances due to differences between the used resampling method und the one of matlab + RTOL = 5e-6 + ATOL = 0 cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) - numpy.testing.assert_allclose(pm.ncm(cleanSig, enhancedSig, fs), expected_vals[14], rtol=rto, atol=atol) + numpy.testing.assert_allclose(pm.ncm(cleanSig, enhancedSig, fs), expected_vals[14], rtol=RTOL, atol=ATOL) @pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_srmr(filePair,expected_vals): + RTOL = 5e-4 + ATOL = 0 cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) ratio,energy = pm.srmr(enhancedSig, fs) numpy.testing.assert_allclose(ratio, expected_vals[15], rtol=RTOL, atol=ATOL) -@pytest.mark.parametrize('filePair,expected_vals', testScenarios) -def test_bsd(filePair,expected_vals): - cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) - bsd = pm.bsd(cleanSig, enhancedSig, fs) - numpy.testing.assert_allclose(bsd, 1e20, rtol=RTOL, atol=ATOL) +#@pytest.mark.parametrize('filePair,expected_vals', testScenarios) +#def test_bsd(filePair,expected_vals): +# cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) +# bsd = pm.bsd(cleanSig, enhancedSig, fs) +# numpy.testing.assert_allclose(bsd, 1e20, rtol=RTOL, atol=ATOL) From cf240ee43847e52bf91d895fda7f0d666f6e4d10 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Mon, 13 Jul 2020 10:45:24 +0200 Subject: [PATCH 64/91] Delete testPerfMetrics.ipynb --- tests/testPerfMetrics.ipynb | 1166 ----------------------------------- 1 file changed, 1166 deletions(-) delete mode 100644 tests/testPerfMetrics.ipynb diff --git a/tests/testPerfMetrics.ipynb b/tests/testPerfMetrics.ipynb deleted file mode 100644 index decdf77..0000000 --- a/tests/testPerfMetrics.ipynb +++ /dev/null @@ -1,1166 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.io import wavfile,loadmat\n", - "from scipy.signal import stft,get_window, correlate\n", - "import librosa\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import sys\n", - "sys.path.append(\"../\") \n", - "\n", - "import pysepm as pm #import fwSNRseg,SNRseg,llr,wss, composite,pesq,stoi" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "include pre-emphasis\n", - "bark filter not tested\n", - "236903.55\n" - ] - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", - "bsd,clean,enh=pm.bsd(cleanSig, enhancedSig, fs)# should be 3.3554\n", - "print(bsd)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "nom=np.sum(np.square(clean-enh),axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "denom=np.sum(np.square(clean),axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "236903.55" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean(nom/denom)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bark filter not tested\n" - ] - } - ], - "source": [ - "a = pm.reverberationMeasures.barks(22050,2048,24)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(a.T);" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0. , 0. , 0. , ..., 0. , 0. ,\n", - " 0. ],\n", - " [0.17526951, 0. , 0. , ..., 0. , 0. ,\n", - " 0. ],\n", - " [0.35053903, 0. , 0. , ..., 0. , 0. ,\n", - " 0. ],\n", - " ...,\n", - " [0. , 0. , 0. , ..., 0. , 0. ,\n", - " 0. ],\n", - " [0. , 0. , 0. , ..., 0. , 0. ,\n", - " 0. ],\n", - " [0. , 0. , 0. , ..., 0. , 0. ,\n", - " 0. ]], dtype=float32)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a.T[0:100,:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 3)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "resultsOrig = loadmat('results.mat')\n", - "resultsOrig=resultsOrig['results']\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "filePairs=[]\n", - "filePairs.append(['speech.wav','speech_bab_0dB.wav'])\n", - "filePairs.append(['sp04.wav','sp04_babble_sn10.wav'])\n", - "filePairs.append(['enhanced.wav','sp04_babble_sn10.wav'])\n", - "filePairs.append(['sp04.wav','enhanced.wav'])\n", - "filePairs.append(['S_03_01.wav','S_03_01_babble_sn0_klt.wav'])\n", - "filePairs.append(['cleanSample_valIdx12.wav','noisySample_valIdx12.wav'])\n", - "filePairs.append(['cleanSample_valIdx12.wav','enhancedSample_valIdx12.wav'])\n", - "filePairs.append(['cleanSample_valIdx356.wav','noisySample_valIdx356.wav'])\n", - "filePairs.append(['cleanSample_valIdx356.wav','enhancedSample_valIdx356.wav'])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.6/dist-packages/srmrpy/hilbert.py:69: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", - " h = h[ind]\n" - ] - } - ], - "source": [ - "results = np.zeros((len(filePairs),16))\n", - "for idx,filePair in enumerate(filePairs):\n", - " fs, cleanSig = wavfile.read(filePair[0])\n", - " fs, enhancedSig = wavfile.read(filePair[1])\n", - " if cleanSig.dtype=='int16':\n", - " cleanSig = cleanSig.astype('float')/abs(np.iinfo(cleanSig.dtype).min)\n", - " if enhancedSig.dtype=='int16':\n", - " enhancedSig = enhancedSig.astype('float')/abs(np.iinfo(enhancedSig.dtype).min)\n", - " \n", - " if cleanSig.shape!=enhancedSig.shape:\n", - " minLen=min((len(cleanSig),len(enhancedSig)))\n", - " cleanSig = cleanSig[0:minLen]\n", - " enhancedSig = enhancedSig[0:minLen]\n", - " cleanSig = cleanSig/np.max(np.abs(cleanSig))\n", - " enhancedSig = enhancedSig/np.max(np.abs(enhancedSig))\n", - " results[idx,0]=pm.fwSNRseg(cleanSig, enhancedSig, fs)\n", - " results[idx,1]=pm.SNRseg(cleanSig, enhancedSig, fs)\n", - " results[idx,2]=pm.llr(cleanSig, enhancedSig, fs)\n", - " results[idx,3]=pm.wss(cleanSig, enhancedSig, fs)\n", - " results[idx,4]=pm.cepstrum_distance(cleanSig, enhancedSig, fs)\n", - " results[idx,5]=pm.stoi(cleanSig, enhancedSig, fs)\n", - " CSIIh,CSIIm,CSIIl=pm.csii(cleanSig, enhancedSig, fs)\n", - " results[idx,6]=CSIIh\n", - " results[idx,7]=CSIIm\n", - " results[idx,8]=CSIIl\n", - " results[idx,9]=CSIIl\n", - " \n", - " if (fs!=16000) | (fs!=8000):\n", - " cleanSig=librosa.core.resample(cleanSig, fs,16000)\n", - " enhancedSig=librosa.core.resample(enhancedSig,fs,16000) \n", - " fs = 16000 \n", - " mos_lqo, pesq_mos = pm.pesq(cleanSig, enhancedSig,fs) \n", - " results[idx,10] = mos_lqo\n", - " Csig,Cbak,Covl=pm.composite(cleanSig, enhancedSig, fs)\n", - " results[idx,11]=Csig\n", - " results[idx,12]=Cbak\n", - " results[idx,13]=Covl\n", - " results[idx,14]=pm.ncm(cleanSig, enhancedSig, fs)\n", - " ratio, energy = pm.srmr(enhancedSig,fs, n_cochlear_filters=23, low_freq=125, min_cf=4, max_cf=128, fast=False)\n", - " results[idx,15]= ratio\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "relErr=(results-resultsOrig)/(resultsOrig+1e-20)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.89866235, 9.87468543, 9.87757872, 8.07671193, -4.58756266,\n", - " 3.43626201, 3.00168894, 0.47358044, 2.37719669])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "relErr[:,-1]*100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# frequency weighted segmental SNR (fwSNRSeg)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "3.355400106582439" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", - "pm.fwSNRseg(cleanSig, enhancedSig, fs)# should be 3.3554\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10.093763940018944" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.fwSNRseg(cleanSig, enhancedSig, fs)# should be 10.0938\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "12.560192001043312" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.fwSNRseg(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 12.5602\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Segmental SNR (SNRSeg)" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-4.0386645840708395" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", - "pm.SNRseg(cleanSig, enhancedSig, fs)# should be -4.0387\n" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.959472693785315" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.SNRseg(cleanSig, enhancedSig, fs)# should be 0.9595\n" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5.281379775527017" - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.SNRseg(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 5.2814\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Log Likelihood Ratio (LLR)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9607521284183373" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", - "pm.llr(cleanSig, enhancedSig, fs)# should be 0.9593\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6399952859426925" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.llr(cleanSig, enhancedSig, fs)# should be 0.6400\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.222739174060473" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.llr(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 0.2227" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Weighted Spectral Slope (WSS)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "52.65786610835284" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", - "pm.wss(cleanSig, enhancedSig, fs)# should be 52.6579" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "37.64901719583485" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.wss(cleanSig, enhancedSig, fs)# should be 37.6490" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19.612652522957717" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.wss(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 19.6127" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PESQ" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0832337141036987" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", - "pm.pesq(cleanSig, enhancedSig, fs)# should be 1.0832" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.463416557463939" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.pesq(cleanSig, enhancedSig, fs)# should be 2.4634" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.7307331381405935" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.pesq(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 2.7293" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Composite Objective Speech Quality Measure" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2.2836551944868853, 1.5287447837866348, 1.6054929873448194)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", - "pm.composite(cleanSig, enhancedSig, fs)# should be (2.2837,1.5287,1.6055)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3.581043880153211, 2.608416773805394, 2.9858296219849687)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.composite(cleanSig, enhancedSig, fs)# should be (3.5810,2.6084,2.9858)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4.333919599483931, 3.1347287982287013, 3.5409091514235116)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.composite(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be (4.3330,3.1340,3.5397)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cepstrum Distance Objective Speech Quality Measure" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6.3889163971985266" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", - "pm.cepstrum_distance(cleanSig, enhancedSig, fs)# should be 6.388916397199216" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4.26593320307941" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.cepstrum_distance(cleanSig, enhancedSig, fs)# should be 4.265933203079426" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.3969895754520847" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.cepstrum_distance(cleanSig, enhancedSig, fs)# should be 2.396989575452212" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# STOI" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6739177895331303" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", - "pm.stoi(cleanSig, enhancedSig, fs)# should be 0.6739" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8934582975768947" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.stoi(cleanSig, enhancedSig, fs)# should be 0.8934" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8916166026077093" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.stoi(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be 0.8917" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "from pesq import pesq\n", - "from scipy.signal import stft,get_window,correlate,resample\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - " numSamples=round(len(cleanSig)/fs*16000)\n", - " fs = 16000\n", - " pesq_mos = pesq(fs,resample(cleanSig, numSamples), resample(enhancedSig, numSamples), 'wb')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# NCM" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5328171511427755" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", - "pm.ncm(cleanSig, enhancedSig, fs)# should be " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9050747979588915" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.ncm(cleanSig, enhancedSig, fs)# should be " - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9633151692925036" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.ncm(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CSII" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.5747598820639694, 0.3343790504514923, 0.02616214182320878)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./speech.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./speech_bab_0dB.wav\")\n", - "pm.csii(cleanSig, enhancedSig, fs)# should be " - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.9121855615693153, 0.622560057685167, 0.20949166842622877)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./sp04.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.csii(cleanSig, enhancedSig, fs)# should be " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.9761576893384923, 0.7825384403451644, 0.6281655003905069)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fs, cleanSig = wavfile.read(\"./enhanced.wav\")\n", - "fs, enhancedSig = wavfile.read(\"./sp04_babble_sn10.wav\")\n", - "pm.csii(cleanSig, enhancedSig[0:len(cleanSig)], fs) # should be " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From f1e1a35eedf9cfff0399ffa005070ad5c5627137 Mon Sep 17 00:00:00 2001 From: schmiph2 Date: Mon, 13 Jul 2020 11:49:18 +0300 Subject: [PATCH 65/91] matlab results for new files --- tests/results.mat | Bin 1202 -> 2450 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/tests/results.mat b/tests/results.mat index f6bc5aca99bd8dc27c3337f1f31e988cb4b108e5..5a00f8233298381e973b412397446f136f05af8d 100644 GIT binary patch delta 2377 zcmV-P3AXmK36c|#G#FHMWgtp*Y#<;vATT*PGB`RlI3O}GGBA-*BavVQk#rD$3JCxJ zc%0SHdpOkj7YFch$z>SV2)P%PQrl&t$T<~$CFFkHWRzq{MIlXUD%EP;QlgTKE~MQe 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l)KdMZhY~y_7k-G=Nx`2Z@)G*X;c`MC%{yHN{{RaS;K68ECa?ei From b0967e6f3fcde2e01d108080e039d42445d2b9ef Mon Sep 17 00:00:00 2001 From: schmiph2 Date: Mon, 13 Jul 2020 12:28:37 +0300 Subject: [PATCH 66/91] removed energy output of srmr --- pysepm/reverberationMeasures.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/pysepm/reverberationMeasures.py b/pysepm/reverberationMeasures.py index 59e0bd9..ff971f6 100755 --- a/pysepm/reverberationMeasures.py +++ b/pysepm/reverberationMeasures.py @@ -9,15 +9,17 @@ def srr_seg(clean_speech, processed_speech,fs): def srmr(speech,fs, n_cochlear_filters=23, low_freq=125, min_cf=4, max_cf=128, fast=False, norm=False): if fs == 8000: - return srmrpy.srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + srmRatio,energy=srmrpy.srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + return srmRatio elif fs == 16000: - return srmrpy.srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) - + srmRatio,energy=srmrpy.srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + return srmRatio else: numSamples=round(len(speech)/fs*16000) fs = 16000 - return srmrpy.srmr(resample(speech, numSamples), fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + srmRatio,energy=srmrpy.srmr(resample(speech, numSamples), fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) + return srmRatio def hz_to_bark(freqs_hz): From 9d102ce9b083ee24ac8d57b9baa2628d27bf7d6e Mon Sep 17 00:00:00 2001 From: schmiph2 Date: Mon, 13 Jul 2020 12:29:09 +0300 Subject: [PATCH 67/91] removed energy output of srmr --- tests/test_metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 0da517a..a7580fd 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -180,7 +180,7 @@ def test_srmr(filePair,expected_vals): RTOL = 5e-4 ATOL = 0 cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair,True,16e3) - ratio,energy = pm.srmr(enhancedSig, fs) + ratio = pm.srmr(enhancedSig, fs) numpy.testing.assert_allclose(ratio, expected_vals[15], rtol=RTOL, atol=ATOL) From 8e2958d78b11f2673c38a6cefe706d248a52a263 Mon Sep 17 00:00:00 2001 From: schmiph2 Date: Mon, 13 Jul 2020 14:11:20 +0300 Subject: [PATCH 68/91] improved execution time of lp based measures using numba --- pysepm/qualityMeasures.py | 63 +++++++++++++++++---------------------- 1 file changed, 27 insertions(+), 36 deletions(-) diff --git a/pysepm/qualityMeasures.py b/pysepm/qualityMeasures.py index 6d3c0aa..05eb96a 100755 --- a/pysepm/qualityMeasures.py +++ b/pysepm/qualityMeasures.py @@ -1,8 +1,9 @@ from scipy.signal import stft,get_window,correlate,resample from scipy.linalg import solve_toeplitz,toeplitz +import scipy import pesq as pypesq # https://github.com/ludlows/python-pesq import numpy as np - +from numba import jit from .util import extract_overlapped_windows def SNRseg(clean_speech, processed_speech,fs, frameLen=0.03, overlap=0.75): @@ -25,7 +26,7 @@ def SNRseg(clean_speech, processed_speech,fs, frameLen=0.03, overlap=0.75): segmental_snr[segmental_snr>MAX_SNR]=MAX_SNR segmental_snr=segmental_snr[:-1] # remove last frame -> not valid return np.mean(segmental_snr) - + def fwSNRseg(cleanSig, enhancedSig, fs, frameLen=0.03, overlap=0.75): if cleanSig.shape!=enhancedSig.shape: raise ValueError('The two signals do not match!') @@ -116,38 +117,23 @@ def fwSNRseg(cleanSig, enhancedSig, fs, frameLen=0.03, overlap=0.75): distortion[distortion>35]=35 return np.mean(distortion) - - -#def lpcoeff(speech_frame, model_order): -# # ---------------------------------------------------------- -# # (1) Compute Autocorrelation Lags -# # ---------------------------------------------------------- -# -# R=correlate(speech_frame,speech_frame) -# R=R[len(speech_frame)-1:len(speech_frame)+model_order] -# # ---------------------------------------------------------- -# # (2) Levinson-Durbin -# # ---------------------------------------------------------- -# lpparams=np.ones((model_order+1)) -# lpparams[1:]=solve_toeplitz(R[0:-1],-R[1:]) -# -# return(lpparams,R) - +@jit def lpcoeff(speech_frame, model_order): + eps=np.finfo(np.float64).eps # ---------------------------------------------------------- # (1) Compute Autocorrelation Lags # ---------------------------------------------------------- winlength = max(speech_frame.shape) - #R = np.zeros((model_order+1,)) - #for k in range(model_order+1): - # if k==0: - # R[k]=np.sum(speech_frame[0:]*speech_frame[0:]) - # else: - # R[k]=np.sum(speech_frame[0:-k]*speech_frame[k:]) - # + R = np.zeros((model_order+1,)) + for k in range(model_order+1): + if k==0: + R[k]=np.sum(speech_frame[0:]*speech_frame[0:]) + else: + R[k]=np.sum(speech_frame[0:-k]*speech_frame[k:]) + - R=correlate(speech_frame,speech_frame) - R=R[len(speech_frame)-1:len(speech_frame)+model_order] + #R=scipy.signal.correlate(speech_frame,speech_frame) + #R=R[len(speech_frame)-1:len(speech_frame)+model_order] # ---------------------------------------------------------- # (2) Levinson-Durbin # ---------------------------------------------------------- @@ -161,12 +147,13 @@ def lpcoeff(speech_frame, model_order): for i in range(0,model_order): a_past[0:i] = a[0:i] - sum_term = sum(a_past[0:i]*R[i:0:-1]) - rcoeff[i]=(R[i+1] - sum_term) / E[i] + sum_term = np.sum(a_past[0:i]*R[i:0:-1]) + rcoeff[i]=(R[i+1] - sum_term) / (E[i]+eps) a[i]=rcoeff[i] - if i==0: - a[0:i] = a_past[0:i] - rcoeff[i]*np.array([]) - else: + #if i==0: + # a[0:i] = a_past[0:i] - rcoeff[i]*np.array([]) + #else: + if i>0: a[0:i] = a_past[0:i] - rcoeff[i]*a_past[i-1::-1] E[i+1]=(1-rcoeff[i]*rcoeff[i])*E[i] @@ -175,7 +162,8 @@ def lpcoeff(speech_frame, model_order): refcoeff = rcoeff; lpparams = np.ones((model_order+1,)) lpparams[1:] = -a - + if np.any(np.isnan(lpparams)): + raise ValueError('123') return(lpparams,R) def llr(clean_speech, processed_speech, fs, used_for_composite=False, frameLen=0.03, overlap=0.75): @@ -203,16 +191,18 @@ def llr(clean_speech, processed_speech, fs, used_for_composite=False, frameLen=0 denominators[ii]=A_clean.dot(toeplitz(R_clean).dot(A_clean.T)) - frac=numerators/denominators + frac=numerators/(denominators) + frac[np.isnan(frac)]=np.inf frac[frac<=0]=1000 distortion = np.log(frac) if not used_for_composite: - distortion[distortion>2]=2 # this line is not in composite measure matlab implementation of loizou + distortion[distortion>2]=2 # this line is not in composite measure but in llr matlab implementation of loizou distortion = np.sort(distortion) distortion = distortion[:int(round(len(distortion)*alpha))] return np.mean(distortion) +@jit def find_loc_peaks(slope,energy): num_crit = len(energy) @@ -386,6 +376,7 @@ def composite(clean_speech, processed_speech, fs): Covl = np.min((5, Covl)) # limit values to [1, 5] return Csig,Cbak,Covl +@jit def lpc2cep(a): # # converts prediction to cepstrum coefficients From e534bbf3f65af656b329629be42040f61be496fc Mon Sep 17 00:00:00 2001 From: schmiph2 Date: Mon, 13 Jul 2020 14:26:14 +0300 Subject: [PATCH 69/91] do not divide by 0 --- pysepm/qualityMeasures.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/pysepm/qualityMeasures.py b/pysepm/qualityMeasures.py index 05eb96a..a50b79d 100755 --- a/pysepm/qualityMeasures.py +++ b/pysepm/qualityMeasures.py @@ -148,7 +148,10 @@ def lpcoeff(speech_frame, model_order): a_past[0:i] = a[0:i] sum_term = np.sum(a_past[0:i]*R[i:0:-1]) - rcoeff[i]=(R[i+1] - sum_term) / (E[i]+eps) + if np.abs(E[i]) < eps: + rcoeff[i]=np.inf + else: + rcoeff[i]=(R[i+1] - sum_term) / (E[i]) a[i]=rcoeff[i] #if i==0: # a[0:i] = a_past[0:i] - rcoeff[i]*np.array([]) @@ -162,8 +165,6 @@ def lpcoeff(speech_frame, model_order): refcoeff = rcoeff; lpparams = np.ones((model_order+1,)) lpparams[1:] = -a - if np.any(np.isnan(lpparams)): - raise ValueError('123') return(lpparams,R) def llr(clean_speech, processed_speech, fs, used_for_composite=False, frameLen=0.03, overlap=0.75): From f86e2fc1ebc11464301d4c4fea6aac2096da27a3 Mon Sep 17 00:00:00 2001 From: schmiph2 Date: Mon, 13 Jul 2020 14:27:09 +0300 Subject: [PATCH 70/91] commented code removed, tolerance for llr slightly increased (due to numba jit) --- tests/test_metrics.py | 36 +----------------------------------- 1 file changed, 1 insertion(+), 35 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index a7580fd..6a68e40 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -28,25 +28,6 @@ pairCounter=pairCounter+1 -#testScenarios.append((['speech.wav','speech_bab_0dB.wav'],resultsOrig[pairCounter,:])) -#pairCounter = pairCounter + 1 -#testScenarios.append((['sp04.wav','sp04_babble_sn10.wav'],resultsOrig[pairCounter,:])) -#pairCounter = pairCounter + 1 -#testScenarios.append((['enhanced.wav','sp04_babble_sn10.wav'],resultsOrig[pairCounter,:])) -#pairCounter = pairCounter + 1 -#testScenarios.append((['sp04.wav','enhanced.wav'],resultsOrig[pairCounter,:])) -#pairCounter = pairCounter + 1 -#testScenarios.append((['S_03_01.wav','S_03_01_babble_sn0_klt.wav'],resultsOrig[pairCounter,:])) -#pairCounter = pairCounter + 1 -#testScenarios.append((['cleanSample_valIdx12.wav','noisySample_valIdx12.wav'],resultsOrig[pairCounter,:])) -#pairCounter = pairCounter + 1 -#testScenarios.append((['cleanSample_valIdx12.wav','enhancedSample_valIdx12.wav'],resultsOrig[pairCounter,:])) -#pairCounter = pairCounter + 1 -#testScenarios.append((['cleanSample_valIdx356.wav','noisySample_valIdx356.wav'],resultsOrig[pairCounter,:])) -#pairCounter = pairCounter + 1 -#testScenarios.append((['cleanSample_valIdx356.wav','enhancedSample_valIdx356.wav'],resultsOrig[pairCounter,:])) - - def load_preprocess_filepair(filePair,resample=False,fs_targ=16000): fs, cleanSig = wavfile.read('data/'+filePair[0]) fs, enhancedSig = wavfile.read('data/'+filePair[1]) @@ -61,28 +42,13 @@ def load_preprocess_filepair(filePair,resample=False,fs_targ=16000): fileNameNoisy="{}_{}_{}_Hz.wav".format(strParts[0],strParts[1],16000) fs, enhancedSig = wavfile.read('data/'+fileNameNoisy) - #fs, cleanSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[0]) - #fs, enhancedSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[1]) elif fs_targ == 16000 and fs == 8000: pass else: - #strParts=filePair[0].split('_') - #fileNameClean="{}_{}_{}_Hz.wav".format(strParts[0],strParts[1],10000) - #fs, cleanSig = wavfile.read('data/'+fileNameClean) - - #strParts=filePair[1].split('_') - #fileNameNoisy="{}_{}_{}_Hz.wav".format(strParts[0],strParts[1],10000) - #fs, enhancedSig = wavfile.read('data/'+fileNameNoisy) cleanSig = librosa.core.resample(cleanSig, fs, fs_targ) enhancedSig = librosa.core.resample(enhancedSig, fs, fs_targ) fs = fs_targ - #fs, cleanSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[0]) - #fs, enhancedSig = wavfile.read('data/'+str(int(fs_targ/1000))+'kHz_'+filePair[1]) - - #cleanSig=librosa.core.resample(cleanSig, fs,fs_targ) - #enhancedSig=librosa.core.resample(enhancedSig,fs,fs_targ) - #fs = fs_targ return cleanSig,enhancedSig,fs @@ -104,7 +70,7 @@ def test_SNRseg(filePair,expected_vals): @pytest.mark.parametrize('filePair,expected_vals', testScenarios) def test_llr(filePair,expected_vals): - RTOL = 5e-9 + RTOL = 5e-8 ATOL = 0 cleanSig,enhancedSig,fs=load_preprocess_filepair(filePair) From ddbe80ab33fb82d7835e77aa04fe5c25e28b97c3 Mon Sep 17 00:00:00 2001 From: schmiph2 Date: Mon, 13 Jul 2020 14:28:12 +0300 Subject: [PATCH 71/91] example for calculating measures added, including wav files --- examples/1_noisySpeech_16000_Hz.wav | Bin 0 -> 638800 bytes examples/1_processed_16000_Hz.wav | Bin 0 -> 638800 bytes examples/1_speech_16000_Hz.wav | Bin 0 -> 638800 bytes examples/examplesForCalculatingMeasures.ipynb | 809 ++++++++++++++++++ 4 files changed, 809 insertions(+) create mode 100644 examples/1_noisySpeech_16000_Hz.wav create mode 100644 examples/1_processed_16000_Hz.wav create mode 100644 examples/1_speech_16000_Hz.wav create mode 100644 examples/examplesForCalculatingMeasures.ipynb diff --git a/examples/1_noisySpeech_16000_Hz.wav b/examples/1_noisySpeech_16000_Hz.wav new file mode 100644 index 0000000000000000000000000000000000000000..cb93d43f7b9848e3ad250bafeedd7f59f0115f94 GIT binary patch literal 638800 zcmWh!c{CSa8@EMvA^RRBp(MihK37soqD`czpOjQ8k`yIdc1n^&q_QMY3Gtoh677;m 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l#&vx$ln$A=<>Za-mp)jPp9$T-J{&sQ2CrQP%v*LJe*-06MTY Date: Mon, 13 Jul 2020 14:29:52 +0300 Subject: [PATCH 72/91] add numba to setup.py --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index 2552b2d..2adbe62 100644 --- a/setup.py +++ b/setup.py @@ -11,6 +11,7 @@ install_requires=[ 'numpy', 'scipy', + 'numba', 'pystoi', 'pesq @ https://github.com/ludlows/python-pesq/archive/master.zip#egg=pesq', 'SRMRpy @ https://github.com/jfsantos/SRMRpy/archive/master.zip#egg=SRMRpy', From cc93b0ad0e356f9b9eb3e40761e5008796548d79 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 14 Jul 2020 14:54:57 +0200 Subject: [PATCH 73/91] Update README.md --- README.md | 19 +++++++------------ 1 file changed, 7 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index c16de8e..8693918 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,15 @@ # pysepm - Speech Enhancement Performance Measures (Quality and Intelligibility) Implemented in Python Python implementation of objective quality and intelligibilty measures mentioned in Philipos C. Loizou's great [Speech Enhancement Book](https://www.crcpress.com/Speech-Enhancement-Theory-and-Practice-Second-Edition/Loizou/p/book/9781138075573). The Python implementations are checked with the MATLAB implementations attached to the book (see [Link](https://crcpress.com/downloads/K14513/K14513_CD_Files.zip)) +# Contribution +If you would like to have an additional measure or if you find a bug/expected behaviour please create an issue. -# Requirements +# Install with pip +Install pysepm: +``` +pip3 install https://github.com/schmiph2/pysepm/archive/master.zip +``` - gcc compiler - cython - numpy - scipy - pystoi - pypesq # Implemented Measures ## Speech Quality Measures + Segmental Signal-to-Noise Ratio (SNRseg) @@ -28,11 +28,6 @@ Python implementation of objective quality and intelligibilty measures mentioned ## Dereverberation Measures (TODO) + Bark spectral distortion (BSD) + Scale-invariant signal to distortion ratio (SI-SDR) -# Install with pip -Install pysepm: -``` -pip3 install https://github.com/schmiph2/pysepm/archive/master.zip -``` # TODO: From ada199d3acb00087e1cd85c2219142692960981c Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 14 Jul 2020 14:56:34 +0200 Subject: [PATCH 74/91] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 8693918..de15725 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,8 @@ Install pysepm: ``` pip3 install https://github.com/schmiph2/pysepm/archive/master.zip ``` +# Example +Please find a Jupyter Notebook with examples for all implemented measures in the [examples folder](https://github.com/schmiph2/pysepm/tree/master/examples). # Implemented Measures ## Speech Quality Measures From 17961a024daddaa66081b431279e6c424b23170f Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Tue, 14 Jul 2020 14:56:52 +0200 Subject: [PATCH 75/91] Update README.md --- README.md | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/README.md b/README.md index de15725..b35801c 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ Install pysepm: ``` pip3 install https://github.com/schmiph2/pysepm/archive/master.zip ``` -# Example +# Examples Please find a Jupyter Notebook with examples for all implemented measures in the [examples folder](https://github.com/schmiph2/pysepm/tree/master/examples). # Implemented Measures @@ -30,8 +30,3 @@ Please find a Jupyter Notebook with examples for all implemented measures in the ## Dereverberation Measures (TODO) + Bark spectral distortion (BSD) + Scale-invariant signal to distortion ratio (SI-SDR) - - -# TODO: -+ systematic tests for all measures -+ change resample method used (e.g. in NCM) to fit matlab implementation From dfdb5ae40e4a608065bd56a4de4eb01639e3416e Mon Sep 17 00:00:00 2001 From: scdp Date: Tue, 14 Jul 2020 18:31:05 +0300 Subject: [PATCH 76/91] bsd calculation adjusted to match matlab implementation --- pysepm/reverberationMeasures.py | 24 +++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/pysepm/reverberationMeasures.py b/pysepm/reverberationMeasures.py index ff971f6..f7010d4 100755 --- a/pysepm/reverberationMeasures.py +++ b/pysepm/reverberationMeasures.py @@ -1,4 +1,5 @@ from scipy.signal import resample,stft +import scipy import srmrpy #https://github.com/jfsantos/SRMRpy import numpy as np from .qualityMeasures import SNRseg @@ -46,7 +47,7 @@ def bark_frequencies(n_barks=128, fmin=0.0, fmax=11025.0): return bark_to_hz(barks) -def barks(fs, n_fft, n_barks=128, fmin=0.0, fmax=None, norm='slaney', dtype=np.float32): +def barks(fs, n_fft, n_barks=128, fmin=0.0, fmax=None, norm='area', dtype=np.float32): if fmax is None: fmax = float(fs) / 2 @@ -73,11 +74,10 @@ def barks(fs, n_fft, n_barks=128, fmin=0.0, fmax=None, norm='slaney', dtype=np.f # .. then intersect them with each other and zero weights[i] = np.maximum(0, np.minimum(lower, upper)) - if norm in (1, 'slaney'): - # Slaney-style bark is scaled to be approx constant energy per channel - enorm = 2.0 / (bark_f[2:n_barks+2] - bark_f[:n_barks]) - weights *= enorm[:, np.newaxis] - print('bark filter not tested') + if norm in (1, 'area'): + weightsPerBand=np.sum(weights,1); + for i in range(weights.shape[0]): + weights[i,:]=weights[i,:]/weightsPerBand[i] return weights def bsd(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): @@ -91,19 +91,21 @@ def bsd(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): print('include pre-emphasis') - hannWin=0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) + hannWin=scipy.signal.windows.hann(winlength)#0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) f,t,Zxx=stft(clean_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=True, boundary=None, padded=False) - clean_power_spec=np.square(np.abs(Zxx)) + clean_power_spec=np.square(np.sum(hannWin)*np.abs(Zxx)) f,t,Zxx=stft(processed_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=True, boundary=None, padded=False) - enh_power_spec=np.square(np.abs(Zxx)) + enh_power_spec=np.square(np.sum(hannWin)*np.abs(Zxx)) bark_filt = barks(fs, n_fft, n_barks=32) clean_power_spec_bark= np.dot(bark_filt,clean_power_spec) enh_power_spec_bark= np.dot(bark_filt,enh_power_spec) + clean_power_spec_bark_2=np.square(clean_power_spec_bark) + diff_power_spec_2 = np.square(clean_power_spec_bark-enh_power_spec_bark) - bsd = np.mean(np.sum(np.square(clean_power_spec_bark-enh_power_spec_bark),axis=0)/np.sum(np.square(clean_power_spec_bark),axis=0)) - return bsd,clean_power_spec_bark,enh_power_spec_bark + bsd = np.mean(np.sum(diff_power_spec_2,axis=0)/np.sum(clean_power_spec_bark_2,axis=0)) + return bsd From 6ceb9887a3a1ab547cf3a1fc0b5f769372705045 Mon Sep 17 00:00:00 2001 From: scdp Date: Tue, 14 Jul 2020 18:31:21 +0300 Subject: [PATCH 77/91] bsd examples added --- examples/examplesForCalculatingMeasures.ipynb | 70 +++++++++++-------- 1 file changed, 41 insertions(+), 29 deletions(-) diff --git a/examples/examplesForCalculatingMeasures.ipynb b/examples/examplesForCalculatingMeasures.ipynb index cccb448..daa000c 100644 --- a/examples/examplesForCalculatingMeasures.ipynb +++ b/examples/examplesForCalculatingMeasures.ipynb @@ -159,7 +159,7 @@ { "data": { "text/plain": [ - "1.2530765533868229" + "1.2530765533868626" ] }, "execution_count": 7, @@ -179,7 +179,7 @@ { "data": { "text/plain": [ - "1.6044180853225458" + "1.6044180854910925" ] }, "execution_count": 8, @@ -253,7 +253,7 @@ { "data": { "text/plain": [ - "7.187803223715054" + "7.187803223674154" ] }, "execution_count": 11, @@ -273,7 +273,7 @@ { "data": { "text/plain": [ - "8.308018500153732" + "8.308018500622689" ] }, "execution_count": 12, @@ -441,7 +441,7 @@ { "data": { "text/plain": [ - "(2.0379920065811965, 1.8630831647556954, 1.5433156477547567)" + "(2.037992006581127, 1.8630831647556954, 1.5433156477547219)" ] }, "execution_count": 19, @@ -484,18 +484,7 @@ "cell_type": "code", "execution_count": 21, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6807573065163481" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pysepm.ncm(clean_speech, noisy_speech, fs)" ] @@ -504,18 +493,7 @@ "cell_type": "code", "execution_count": 22, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5724369059533503" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pysepm.ncm(clean_speech, enhanced_speech, fs)" ] @@ -575,6 +553,40 @@ "pysepm.srmr(enhanced_speech, fs)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# BSD" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "include pre-emphasis\n", + "bark filter not tested\n" + ] + } + ], + "source": [ + "bsd=pysepm.bsd(clean_speech, noisy_speech, fs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bsd=pysepm.bsd(clean_speech, enhanced_speech, fs)" + ] + }, { "cell_type": "markdown", "metadata": {}, From ae2fe3d1d809c927e089dc4f2ffe1060e60c1b28 Mon Sep 17 00:00:00 2001 From: scdp Date: Tue, 14 Jul 2020 18:43:30 +0300 Subject: [PATCH 78/91] pre-emphasis added to bsd --- pysepm/reverberationMeasures.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/pysepm/reverberationMeasures.py b/pysepm/reverberationMeasures.py index f7010d4..81dd38f 100755 --- a/pysepm/reverberationMeasures.py +++ b/pysepm/reverberationMeasures.py @@ -82,14 +82,18 @@ def barks(fs, n_fft, n_barks=128, fmin=0.0, fmax=None, norm='area', dtype=np.flo def bsd(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): + pre_emphasis_coeff = 0.95 + b = np.array([1]) + a = np.array([1,pre_emphasis_coeff]) + clean_speech = scipy.signal.lfilter(b,a,clean_speech) + processed_speech = scipy.signal.lfilter(b,a,processed_speech) + winlength = round(frameLen*fs) #window length in samples skiprate = int(np.floor((1-overlap)*frameLen*fs)) #window skip in samples max_freq = fs/2 #maximum bandwidth n_fft = 2**np.ceil(np.log2(2*winlength)) n_fftby2 = int(n_fft/2) num_frames = len(clean_speech)/skiprate-(winlength/skiprate)# number of frames - - print('include pre-emphasis') hannWin=scipy.signal.windows.hann(winlength)#0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) f,t,Zxx=stft(clean_speech[0:int(num_frames)*skiprate+int(winlength-skiprate)], fs=fs, window=hannWin, nperseg=winlength, noverlap=winlength-skiprate, nfft=n_fft, detrend=False, return_onesided=True, boundary=None, padded=False) From 9905e812282c72eda08e65ea54772a29840c13ec Mon Sep 17 00:00:00 2001 From: scdp Date: Tue, 14 Jul 2020 18:44:41 +0300 Subject: [PATCH 79/91] results in examples changed --- examples/examplesForCalculatingMeasures.ipynb | 220 +++++++++++++++++- 1 file changed, 213 insertions(+), 7 deletions(-) diff --git a/examples/examplesForCalculatingMeasures.ipynb b/examples/examplesForCalculatingMeasures.ipynb index daa000c..4bb8e46 100644 --- a/examples/examplesForCalculatingMeasures.ipynb +++ b/examples/examplesForCalculatingMeasures.ipynb @@ -562,29 +562,56 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "include pre-emphasis\n", - "bark filter not tested\n" + "include pre-emphasis\n" ] + }, + { + "data": { + "text/plain": [ + "38086949616.83964" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "bsd=pysepm.bsd(clean_speech, noisy_speech, fs)" + "pysepm.bsd(clean_speech, noisy_speech, fs)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "include pre-emphasis\n" + ] + }, + { + "data": { + "text/plain": [ + "51047508.139671564" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "bsd=pysepm.bsd(clean_speech, enhanced_speech, fs)" + "pysepm.bsd(clean_speech, enhanced_speech, fs)" ] }, { @@ -789,6 +816,185 @@ "%timeit pysepm.srmr(clean_speech, fs)" ] }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "include pre-emphasis\n", + "bark filter not tested\n", + "37.9 ms ± 319 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%timeit pysepm.bsd(clean_speech, noisy_speech, fs)" + ] + }, { "cell_type": "code", "execution_count": null, From 78b9a3a476c3087666fcbf22403adc65f62d4c52 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 27 Aug 2020 10:08:48 +0200 Subject: [PATCH 80/91] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b35801c..56dc548 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# pysepm - Speech Enhancement Performance Measures (Quality and Intelligibility) Implemented in Python +# pysepm - Python Speech Enhancement Performance Measures (Quality and Intelligibility) Python implementation of objective quality and intelligibilty measures mentioned in Philipos C. Loizou's great [Speech Enhancement Book](https://www.crcpress.com/Speech-Enhancement-Theory-and-Practice-Second-Edition/Loizou/p/book/9781138075573). The Python implementations are checked with the MATLAB implementations attached to the book (see [Link](https://crcpress.com/downloads/K14513/K14513_CD_Files.zip)) # Contribution From 16f5e2b9652ddbe0bd4e32d8a453762a9220d720 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 10 Sep 2020 08:45:10 +0200 Subject: [PATCH 81/91] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 56dc548..b1e9e3a 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,5 @@ # pysepm - Python Speech Enhancement Performance Measures (Quality and Intelligibility) +[![DOI](https://zenodo.org/badge/220233987.svg)](https://zenodo.org/badge/latestdoi/220233987) Python implementation of objective quality and intelligibilty measures mentioned in Philipos C. Loizou's great [Speech Enhancement Book](https://www.crcpress.com/Speech-Enhancement-Theory-and-Practice-Second-Edition/Loizou/p/book/9781138075573). The Python implementations are checked with the MATLAB implementations attached to the book (see [Link](https://crcpress.com/downloads/K14513/K14513_CD_Files.zip)) # Contribution From 30d3bc8b42ebe49b50e6ed8782a68628dafc5423 Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Thu, 10 Sep 2020 08:45:21 +0200 Subject: [PATCH 82/91] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index b1e9e3a..f996c29 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ # pysepm - Python Speech Enhancement Performance Measures (Quality and Intelligibility) [![DOI](https://zenodo.org/badge/220233987.svg)](https://zenodo.org/badge/latestdoi/220233987) + Python implementation of objective quality and intelligibilty measures mentioned in Philipos C. Loizou's great [Speech Enhancement Book](https://www.crcpress.com/Speech-Enhancement-Theory-and-Practice-Second-Edition/Loizou/p/book/9781138075573). The Python implementations are checked with the MATLAB implementations attached to the book (see [Link](https://crcpress.com/downloads/K14513/K14513_CD_Files.zip)) # Contribution From bb0d2e4ea1a0e7c6a53ea3994e4a290ba788955b Mon Sep 17 00:00:00 2001 From: scdp Date: Tue, 17 Nov 2020 09:13:03 +0100 Subject: [PATCH 83/91] new import for changed pystoi library --- pysepm/intelligibilityMeasures.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pysepm/intelligibilityMeasures.py b/pysepm/intelligibilityMeasures.py index f54fda9..c181dd1 100755 --- a/pysepm/intelligibilityMeasures.py +++ b/pysepm/intelligibilityMeasures.py @@ -1,11 +1,11 @@ from scipy.signal import stft,resample,butter,lfilter,hilbert from scipy.interpolate import interp1d -import pystoi.stoi # https://github.com/mpariente/pystoi +from pystoi import stoi as pystoi # https://github.com/mpariente/pystoi import numpy as np from .util import extract_overlapped_windows,resample_matlab_like -stoi = pystoi.stoi +stoi = pystoi def fwseg_noise(clean_speech, processed_speech,fs,frameLen=0.03, overlap=0.75): From df7c2aa37832ccc455b961155c9a89fd858dcdc7 Mon Sep 17 00:00:00 2001 From: scdp Date: Tue, 17 Nov 2020 17:03:43 +0100 Subject: [PATCH 84/91] update implementation to match matlab reference --- pysepm/qualityMeasures.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/pysepm/qualityMeasures.py b/pysepm/qualityMeasures.py index a50b79d..726714c 100755 --- a/pysepm/qualityMeasures.py +++ b/pysepm/qualityMeasures.py @@ -148,10 +148,12 @@ def lpcoeff(speech_frame, model_order): a_past[0:i] = a[0:i] sum_term = np.sum(a_past[0:i]*R[i:0:-1]) - if np.abs(E[i]) < eps: - rcoeff[i]=np.inf + + if E[i]==0.0: # prevents zero division error, numba doesn't allow try/except statements + rcoeff[i]= np.inf else: rcoeff[i]=(R[i+1] - sum_term) / (E[i]) + a[i]=rcoeff[i] #if i==0: # a[0:i] = a_past[0:i] - rcoeff[i]*np.array([]) @@ -178,8 +180,8 @@ def llr(clean_speech, processed_speech, fs, used_for_composite=False, frameLen=0 P = 16 # this could vary depending on sampling frequency. hannWin=0.5*(1-np.cos(2*np.pi*np.arange(1,winlength+1)/(winlength+1))) - clean_speech_framed=extract_overlapped_windows(clean_speech,winlength,winlength-skiprate,hannWin) - processed_speech_framed=extract_overlapped_windows(processed_speech,winlength,winlength-skiprate,hannWin) + clean_speech_framed=extract_overlapped_windows(clean_speech+eps,winlength,winlength-skiprate,hannWin) + processed_speech_framed=extract_overlapped_windows(processed_speech+eps,winlength,winlength-skiprate,hannWin) numFrames=clean_speech_framed.shape[0] numerators = np.zeros((numFrames-1,)) denominators = np.zeros((numFrames-1,)) From fff03d07d93b1c423df621314673e0141d61efbd Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Fri, 20 Nov 2020 13:16:50 +0100 Subject: [PATCH 85/91] Update README.md --- README.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/README.md b/README.md index f996c29..16be641 100644 --- a/README.md +++ b/README.md @@ -3,9 +3,6 @@ Python implementation of objective quality and intelligibilty measures mentioned in Philipos C. Loizou's great [Speech Enhancement Book](https://www.crcpress.com/Speech-Enhancement-Theory-and-Practice-Second-Edition/Loizou/p/book/9781138075573). The Python implementations are checked with the MATLAB implementations attached to the book (see [Link](https://crcpress.com/downloads/K14513/K14513_CD_Files.zip)) -# Contribution -If you would like to have an additional measure or if you find a bug/expected behaviour please create an issue. - # Install with pip Install pysepm: ``` From 3c3f35ef5846d0e976adbc9d72469c3d4ae99a4f Mon Sep 17 00:00:00 2001 From: schmiph2 <43240826+schmiph2@users.noreply.github.com> Date: Wed, 30 Jun 2021 18:15:29 +0200 Subject: [PATCH 86/91] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 16be641..28b74f4 100644 --- a/README.md +++ b/README.md @@ -17,12 +17,12 @@ Please find a Jupyter Notebook with examples for all implemented measures in the + Frequency-weighted Segmental SNR (fwSNRseg) + Log-likelihood Ratio (LLR) + Weighted Spectral Slope (WSS) -+ Perceptual Evaluation of Speech Quality (PESQ) ++ Perceptual Evaluation of Speech Quality (PESQ), ([python-pesq](https://github.com/ludlows/python-pesq) implementation by ludlows) + Composite Objective Speech Quality (composite) + Cepstrum Distance Objective Speech Quality Measure (CD) ## Speech Intelligibility Measures -+ Short-time objective intelligibility (STOI) ++ Short-time objective intelligibility (STOI), ([pystoi](https://github.com/mpariente/pystoi) implementation by mpariente) + Coherence and speech intelligibility index (CSII) + Normalized-covariance measure (NCM) From 0a4bdb74507dab55ef3d0a4bed2ecbbd02ef3efb Mon Sep 17 00:00:00 2001 From: HTill <89354430+HTill@users.noreply.github.com> Date: Mon, 22 Jan 2024 19:09:30 +0100 Subject: [PATCH 87/91] Update qualityMeasures.py It should be possible to calculate the narrowband pesq with a signale sampled at 16 kHz. --- pysepm/qualityMeasures.py | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/pysepm/qualityMeasures.py b/pysepm/qualityMeasures.py index 726714c..f2c657c 100755 --- a/pysepm/qualityMeasures.py +++ b/pysepm/qualityMeasures.py @@ -344,15 +344,24 @@ def wss(clean_speech, processed_speech, fs, frameLen=0.03, overlap=0.75): distortion = distortion[:int(round(len(distortion)*alpha))] return np.mean(distortion) -def pesq(clean_speech, processed_speech, fs): - if fs == 8000: - mos_lqo = pypesq.pesq(fs,clean_speech, processed_speech, 'nb') +def pesq(clean_speech, processed_speech, fs, mode=None): + if (fs != 8000) and (fs != 16000): + raise ValueError('fs must be either 8 kHz or 16 kHz') + + if mode is None: + if fs == 8000: + mode = 'nb' + elif fs == 16000: + mode = 'wb' + + if mode == 'nb': + mos_lqo = pypesq.pesq(fs,clean_speech, processed_speech, mode) pesq_mos = 46607/14945 - (2000*np.log(1/(mos_lqo/4 - 999/4000) - 1))/2989#0.999 + ( 4.999-0.999 ) / ( 1+np.exp(-1.4945*pesq_mos+4.6607) ) - elif fs == 16000: - mos_lqo = pypesq.pesq(fs,clean_speech, processed_speech, 'wb') + elif mode == 'wb': + mos_lqo = pypesq.pesq(fs,clean_speech, processed_speech, mode) pesq_mos = np.NaN else: - raise ValueError('fs must be either 8 kHz or 16 kHz') + print('mode should be "nb" for narrowband or "wb" for wideband') return pesq_mos,mos_lqo @@ -434,4 +443,4 @@ def cepstrum_distance(clean_speech, processed_speech, fs, frameLen=0.03, overlap IS_len= round( len( IS_dist)* alpha) IS = np.sort(IS_dist) cep_mean= np.mean( IS[ 0: IS_len]) - return cep_mean \ No newline at end of file + return cep_mean From d9a9ea58a8a6099fcc0414df6d6bac22b8e3cc48 Mon Sep 17 00:00:00 2001 From: HTill <89354430+HTill@users.noreply.github.com> Date: Wed, 12 Jun 2024 13:07:05 +0200 Subject: [PATCH 88/91] Update util.py --- pysepm/util.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pysepm/util.py b/pysepm/util.py index 608ba20..4da85c3 100755 --- a/pysepm/util.py +++ b/pysepm/util.py @@ -1,5 +1,6 @@ import numpy as np -from scipy.signal import firls,kaiser,upfirdn +from scipy.signal import firls,upfirdn +from scipy.signal.windows import kaiser from fractions import Fraction def extract_overlapped_windows(x,nperseg,noverlap,window=None): @@ -52,4 +53,4 @@ def resample_matlab_like(x_orig,p,q): if x_orig.shape[0] Date: Wed, 12 Jun 2024 13:14:48 +0200 Subject: [PATCH 89/91] Update reverberationMeasures.py --- pysepm/reverberationMeasures.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/pysepm/reverberationMeasures.py b/pysepm/reverberationMeasures.py index 81dd38f..6a400c6 100755 --- a/pysepm/reverberationMeasures.py +++ b/pysepm/reverberationMeasures.py @@ -1,6 +1,6 @@ from scipy.signal import resample,stft import scipy -import srmrpy #https://github.com/jfsantos/SRMRpy +#import srmrpy #https://github.com/jfsantos/SRMRpy import numpy as np from .qualityMeasures import SNRseg @@ -8,19 +8,19 @@ def srr_seg(clean_speech, processed_speech,fs): return SNRseg(clean_speech, processed_speech,fs) -def srmr(speech,fs, n_cochlear_filters=23, low_freq=125, min_cf=4, max_cf=128, fast=False, norm=False): - if fs == 8000: - srmRatio,energy=srmrpy.srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) - return srmRatio - - elif fs == 16000: - srmRatio,energy=srmrpy.srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) - return srmRatio - else: - numSamples=round(len(speech)/fs*16000) - fs = 16000 - srmRatio,energy=srmrpy.srmr(resample(speech, numSamples), fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) - return srmRatio +#def srmr(speech,fs, n_cochlear_filters=23, low_freq=125, min_cf=4, max_cf=128, fast=False, norm=False): +# if fs == 8000: +# srmRatio,energy=srmrpy.srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) +# return srmRatio +# +# elif fs == 16000: +# srmRatio,energy=srmrpy.srmr(speech, fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) +# return srmRatio +# else: +# numSamples=round(len(speech)/fs*16000) +# fs = 16000 +# srmRatio,energy=srmrpy.srmr(resample(speech, numSamples), fs, n_cochlear_filters=n_cochlear_filters, low_freq=low_freq, min_cf=min_cf, max_cf=max_cf, fast=fast, norm=norm) +# return srmRatio def hz_to_bark(freqs_hz): From a6e8110b430a70f61a8eaab2e8b7120951c6f39a Mon Sep 17 00:00:00 2001 From: HTill <89354430+HTill@users.noreply.github.com> Date: Wed, 12 Jun 2024 13:16:46 +0200 Subject: [PATCH 90/91] Update __init__.py --- pysepm/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pysepm/__init__.py b/pysepm/__init__.py index 2fc724e..f2a63cd 100755 --- a/pysepm/__init__.py +++ b/pysepm/__init__.py @@ -3,6 +3,6 @@ from .qualityMeasures import fwSNRseg,SNRseg,llr,wss,composite,pesq,cepstrum_distance from .intelligibilityMeasures import stoi,csii,ncm -from .reverberationMeasures import srr_seg,bsd,srmr +from .reverberationMeasures import srr_seg,bsd,#srmr From 984b89ac0b30c4a9618328e518b1c7d7f95ffb08 Mon Sep 17 00:00:00 2001 From: HTill <89354430+HTill@users.noreply.github.com> Date: Wed, 12 Jun 2024 13:17:27 +0200 Subject: [PATCH 91/91] Update __init__.py --- pysepm/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pysepm/__init__.py b/pysepm/__init__.py index f2a63cd..c8ff390 100755 --- a/pysepm/__init__.py +++ b/pysepm/__init__.py @@ -3,6 +3,6 @@ from .qualityMeasures import fwSNRseg,SNRseg,llr,wss,composite,pesq,cepstrum_distance from .intelligibilityMeasures import stoi,csii,ncm -from .reverberationMeasures import srr_seg,bsd,#srmr +from .reverberationMeasures import srr_seg,bsd#,srmr

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