DeepBach: a Steerable Model for Bach chorales generation

DeepBach: a Steerable Model for Bach chorales generation by Gaëtan Hadjeres and François Pachet.

Abstract:

The composition of polyphonic chorale music in the style of J.S Bach has represented a major challenge in automatic music composition over the last decades. The art of Bach chorales composition involves combining four-part harmony with characteristic rhythmic patterns and typical melodic movements to produce musical phrases which begin, evolve and end (cadences) in a harmonious way. To our knowledge, no model so far was able to solve all these problems simultaneously using an agnostic machine-learning approach. This paper introduces DeepBach, a statistical model aimed at modeling polyphonic music and specifically four parts, 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. We evaluate how indistinguishable our generated chorales are from existing Bach chorales with a listening test. The results corroborate our claim. A key strength of DeepBach is that it is agnostic and flexible. Users can constrain the generation by imposing some notes, rhythms or cadences in the generated score. This allows users to reharmonize user-defined melodies. DeepBach’s generation is fast, making it usable for interactive music composition applications. Several generation examples are provided and discussed from a musical point of view.

Take this with you on January 20, 2017 in case you tire of playing #DisruptJ20 Twitter Game (guessing XQuery/XPath definitions). Unlikely I know but anything can happen.

Deeply impressive work.

You can hear samples at:


http://www.flow-machines.com/deepbach-steerable-model-bach-choralesgeneration/

Download the code:

https://github.com/SonyCSL-Paris/DeepBach

Makes me curious about the composition of “like” works for composers who left smaller corpora.

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