Autoregressive LSTM with attention yields the most coherent Bach-style samples; vector quantization improves VAE structure over standard recurrent VAEs while GANs struggle with training stability and style generalization.
Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach
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abstract
A model of music needs to have the ability to recall past details and have a clear, coherent understanding of musical structure. Detailed in the paper is a deep reinforcement learning architecture that predicts and generates polyphonic music aligned with musical rules. The probabilistic model presented is a Bi-axial LSTM trained with a pseudo-kernel reminiscent of a convolutional kernel. To encourage exploration and impose greater global coherence on the generated music, a deep reinforcement learning approach DQN is adopted. When analyzed quantitatively and qualitatively, this approach performs well in composing polyphonic music.
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Generative Modeling of Bach-Style Symbolic Music: A Comparative Study of Autoregressive, Latent-Variable, and Adversarial Approaches
Autoregressive LSTM with attention yields the most coherent Bach-style samples; vector quantization improves VAE structure over standard recurrent VAEs while GANs struggle with training stability and style generalization.