  As deep learning advances, algorithms of music composition increase in
performance. However, most of the successful models are designed for specific
musical structures. Here, we present BachProp, an algorithmic composer that can
generate music scores in many styles given sufficient training data. To adapt
BachProp to a broad range of musical styles, we propose a novel representation
of music and train a deep network to predict the note transition probabilities
of a given music corpus. In this paper, new music scores generated by BachProp
are compared with the original corpora as well as with different network
architectures and other related models. We show that BachProp captures
important features of the original datasets better than other models and invite
the reader to a qualitative comparison on a large collection of generated
songs.
