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RL-Chord: CLSTM-Based Melody Harmonization Using Deep Reinforcement Learning.

Shulei Ji, Xinyu Yang, Jing Luo

    IEEE Transactions on Neural Networks and Learning Systems
    |April 7, 2023
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    Summary
    This summary is machine-generated.

    This study introduces RL-Chord, a novel reinforcement learning system for automatic melody harmonization. It generates high-quality chord progressions, outperforming previous methods and demonstrating effectiveness in Chinese folk music harmonization.

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    Area of Science:

    • Artificial Intelligence
    • Music Information Retrieval
    • Computational Musicology

    Background:

    • Automatic music generation, particularly melody harmonization, is a challenging task in artificial intelligence.
    • Previous recurrent neural network (RNN) approaches struggle with long-term dependencies and lack music theory integration.
    • Existing chord representations can be cumbersome and difficult to expand.

    Purpose of the Study:

    • To propose a novel melody harmonization system, RL-Chord, leveraging reinforcement learning (RL) for high-quality chord progression generation.
    • To develop a universal chord representation that is compact and easily expandable.
    • To evaluate and compare different RL algorithms for melody harmonization and adapt the system for specific musical styles.

    Main Methods:

    • Devised a universal chord representation with a fixed, small dimension.
    • Developed a melody conditional LSTM (CLSTM) model to learn chord transitions and durations.
    • Integrated RL algorithms, specifically comparing policy gradient, Q-learning, and actor-critic, with a deep Q-network (DQN) proving its superiority.
    • Introduced a style classifier for zero-shot Chinese folk (CF) melody harmonization fine-tuning.

    Main Results:

    • The proposed RL-Chord system, particularly the DQN-based approach, generates harmonious and fluent chord progressions.
    • Demonstrated superior performance compared to existing methods across multiple evaluation metrics: chord histogram similarity (CHS), chord tonal distance (CTD), and melody-chord tonal distance (MCTD).
    • Successfully adapted the model for zero-shot Chinese folk melody harmonization using a style classifier.

    Conclusions:

    • RL-Chord offers a significant advancement in automatic melody harmonization by effectively combining deep learning and reinforcement learning with music theory principles.
    • The deep Q-network approach provides a robust and superior method for generating musically coherent chord progressions.
    • The system's adaptability to specific styles, like Chinese folk music, highlights its potential for diverse applications in music generation.