  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.
