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We introduce MIDI-VAE, a neural network model based on VariationalAutoencoders that is capable of handling polyphonic music with multipleinstrument tracks, as well as modeling the dynamics of music by incorporatingnote durations and velocities. We show that MIDI-VAE can perform style transferon symbolic music by automatically changing pitches, dynamics and instrumentsof a music piece from, e.g., a Classical to a Jazz style. We evaluate theefficacy of the style transfer by training separate style validationclassifiers. Our model can also interpolate between short pieces of music,produce medleys and create mixtures of entire songs. The interpolationssmoothly change pitches, dynamics and instrumentation to create a harmonicbridge between two music pieces. To the best of our knowledge, this workrepresents the first successful attempt at applying neural style transfer tocomplete musical compositions.

MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer