Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach
Sound
2018-12-05 v1 Machine Learning
Audio and Speech Processing
Machine Learning
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.
Cite
@article{arxiv.1812.01060,
title = {Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach},
author = {Nikhil Kotecha},
journal= {arXiv preprint arXiv:1812.01060},
year = {2018}
}
Comments
42 pages