Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach

Understand

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.