  Recent advances in deep neural networks have enabled algorithms to compose
music that is comparable to music composed by humans. However, few algorithms
allow the user to generate music with tunable parameters. The ability to tune
properties of generated music will yield more practical benefits for aiding
artists, filmmakers, and composers in their creative tasks. In this paper, we
introduce DeepJ - an end-to-end generative model that is capable of composing
music conditioned on a specific mixture of composer styles. Our innovations
include methods to learn musical style and music dynamics. We use our model to
demonstrate a simple technique for controlling the style of generated music as
a proof of concept. Evaluation of our model using human raters shows that we
have improved over the Biaxial LSTM approach.
