Generating Music using an LSTM Network
Sound
2018-04-23 v1 Machine Learning
Audio and Speech Processing
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 neural network architecture that predicts and generates polyphonic music aligned with musical rules. The probabilistic model presented is a Bi-axial LSTM trained with a kernel reminiscent of a convolutional kernel. When analyzed quantitatively and qualitatively, this approach performs well in composing polyphonic music. Link to the code is provided.
Cite
@article{arxiv.1804.07300,
title = {Generating Music using an LSTM Network},
author = {Nikhil Kotecha and Paul Young},
journal= {arXiv preprint arXiv:1804.07300},
year = {2018}
}
Comments
8 pages, 11 figures