DeepBach: a Steerable Model for Bach Chorales Generation

Gaëtan Hadjeres, François Pachet, Frank Nielsen
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1362-1371, 2017.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v70-hadjeres17a, title = {{D}eep{B}ach: a Steerable Model for {B}ach Chorales Generation}, author = {Ga{\"e}tan Hadjeres and Fran{\c{c}}ois Pachet and Frank Nielsen}, booktitle = {Proceedings of the 34th International Conference on Machine Learning}, pages = {1362--1371}, year = {2017}, editor = {Precup, Doina and Teh, Yee Whye}, volume = {70}, series = {Proceedings of Machine Learning Research}, month = {06--11 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v70/hadjeres17a/hadjeres17a.pdf}, url = {https://proceedings.mlr.press/v70/hadjeres17a.html}, 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.} }
Endnote
%0 Conference Paper %T DeepBach: a Steerable Model for Bach Chorales Generation %A Gaëtan Hadjeres %A François Pachet %A Frank Nielsen %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-hadjeres17a %I PMLR %P 1362--1371 %U https://proceedings.mlr.press/v70/hadjeres17a.html %V 70 %X 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.
APA
Hadjeres, G., Pachet, F. & Nielsen, F.. (2017). DeepBach: a Steerable Model for Bach Chorales Generation. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:1362-1371 Available from https://proceedings.mlr.press/v70/hadjeres17a.html.

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