Hadjeres, G., Pachet, F. and Nielsen, F. DeepBach: a Steerable Model for Bach Chorales Generation. In Doina Precup and Yee Whye Teh, editor, Proceedings of the 34th International Conference on Machine Learning, Proceedings of Machine Learning Research (vol. 70), pages 1362-1371, International Convention Centre, Sydney, Australia, August 2017 PMLR.

Sony CSL authors: François Pachet

Abstract

This paper introduces DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces. We claim that, after being trained on the chorale harmonizations by Johann Sebastian Bach, our model is capable of generating highly convincing chorales in the style of Bach. DeepBach’s strength comes from the use of pseudo-Gibbs sampling coupled with an adapted representation of musical data. This is in contrast with many automatic music composition approaches which tend to compose music sequentially. Our model is also steerable in the sense that a user can constrain the generation by imposing positional constraints such as notes, rhythms or cadences in the generated score. We also provide a plugin on top of the MuseScore music editor making the interaction with DeepBach easy to use.

Keywords: Machine, Learning

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BibTeX entry

@INPROCEEDINGS { hadjeres:17a, ADDRESS="International Convention Centre, Sydney, Australia", AUTHOR="Hadjeres, G. and Pachet, F. and Nielsen, F.", BOOKTITLE="Proceedings of the 34th International Conference on Machine Learning", EDITOR="Doina Precup and Yee Whye Teh", MONTH="August", PAGES="1362--1371", PUBLISHER="PMLR", SERIES="Proceedings of Machine Learning Research", TITLE="DeepBach: a Steerable Model for Bach Chorales Generation", VOLUME="70", YEAR="2017", }