Music Generation Using Bayesian Networks
Abstract
Music generation has recently become popular as an application of machine learning. To generate polyphonic music, one must consider both simultaneity (the vertical consistency) and sequentiality (the horizontal consistency). Bayesian networks are suitable to model both simultaneity and sequentiality simultaneously. Here, we present music generation models based on Bayesian networks applied to chord voicing, four-part harmonization, and real-time chord prediction.
Cite
Text
Kitahara. "Music Generation Using Bayesian Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71273-4_33Markdown
[Kitahara. "Music Generation Using Bayesian Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/kitahara2017ecmlpkdd-music/) doi:10.1007/978-3-319-71273-4_33BibTeX
@inproceedings{kitahara2017ecmlpkdd-music,
title = {{Music Generation Using Bayesian Networks}},
author = {Kitahara, Tetsuro},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {368-372},
doi = {10.1007/978-3-319-71273-4_33},
url = {https://mlanthology.org/ecmlpkdd/2017/kitahara2017ecmlpkdd-music/}
}