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Hand in hand with deep learning advancements, algorithms of music compositionincrease in performance. However, most of the successful models are designedfor specific musical structures. Here, we present BachProp, an algorithmiccomposer that can generate music scores in any style given sufficient trainingdata. To adapt BachProp to a broad range of musical styles, we propose a novelnormalized representation of music and train a deep network to predict the notetransition probabilities of a given music corpus. In this paper, new musicscores sampled by BachProp are compared with the original corpora viacrowdsourcing. This evaluation indicates that the music scores generated byBachProp are not less preferred than the original music corpus the algorithmwas provided with.

BachProp: Learning to Compose Music in Multiple Styles