MusGen

Generate Music using Deep Learning

Overview

MusGen is a deep learning-based project aimed at generating music using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) architectures. The core objective is to develop a model that generates musically coherent sequences adhering to basic principles of music theory such as key, time signature, rhythm, and phrase structure.

This project lies at the intersection of Music Information Retrieval (MIR), audio signal processing, deep learning, and cognitive psychology.

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Objectives

• Design a suitable neural network architecture (initially RNNs and LSTMs) for melody generation.
• Train the model on a structured music dataset.
• Incorporate musical structure into the generation process—such as key, tempo (BPM), and phrase boundaries.
• Address common issues in LSTM-based music generation like lack of macro-periodicity (repetition of verses, choruses, etc.).
• Evaluate generated music using both quantitative and qualitative metrics.

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Initial Preparation

Before model development, familiarity with basic music theory is essential.

• 🎵 Music Theory Primer: YouTube Link
• 🎧 Audio Processing Notes: Google Drive PDF

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What Makes Music Different from Audio?

Key features that distinguish music from arbitrary audio signals include:

• Rhythm and elements that repeat in time (e.g., every bar)
• Key and Scale (though not always explicitly followed)
• Tempo (BPM)
• Frequency ranges for different instruments (bass, mids, highs)
• Song structure: verses, choruses, bridges, intros, etc.

Traditional LSTM models often fail to capture macro-periodicity—i.e., structured repetition such as returning to the chorus or predictable transitions between sections.

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Capturing Musical Structure

To improve structural coherence and periodicity in generated music, the following methods may be explored:

🔁 Hierarchical Models

• Allow modeling of long-range dependencies (e.g., transitions between chorus and verse).

🎯 Attention Mechanisms

• Help focus on important musical elements from earlier in the sequence.

🔄 Markov Models / Hidden Markov Models (HMMs)

• Model state transitions that correspond to different song sections (verse → chorus → bridge).

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Evaluation Metrics

Unlike conventional ML tasks, music generation requires domain-specific evaluation metrics. Below are three proposed metrics:

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1. Rhythmic Regularity

Goal: Measure how consistently the generated melody follows a beat (BPM).

Method:

• Detect beat onsets using manual tapping or an Onset Detection Function (ODF).
• Compute time intervals between successive beat onsets.
• Calculate the variance of these intervals.

Metric:

• $\text{Var}(\Delta t)$
• Lower variance → more rhythmic consistency.

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2. Melodic Contour Smoothness

Goal: Assess the naturalness of pitch transitions.

Method:

• Represent melody as a sequence of pitch values: $P = {p_1, p_2, \dots, p_n}$
• Compute pitch deltas: $\Delta p_i = |p_{i+1} - p_i|$
• Analyze mean and variance of $\Delta p$

Metric:

• $\mu(\Delta p), \sigma^2(\Delta p)$
• Lower values → smoother melodic transitions.

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3. Phrase Structure Coherence

Goal: Evaluate organization into meaningful musical phrases.

Method:

• Segment the melody based on rhythmic/melodic cues or harmonic resolution.
• Identify clear phrase boundaries (e.g., similar motifs starting or ending phrases).
• Compare patterns across segments.

Metric:

• Mostly qualitative; may involve structured rubric-based human evaluation or motif matching algorithms.

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Conclusion

MusGen aims to bridge the gap between deep learning and structured music composition. By incorporating music theory and structural modeling techniques, the project aspires to generate musically rich and meaningful compositions that go beyond simple note sequences.