Hierarchical Variational Autoencoders for Music


In this work we develop recurrent variational autoencoders (VAEs) trained to reproduce short musical sequences and demonstrate their use as a creative device both via random sampling and data interpolation. Furthermore, by using a novel hierarchical decoder, we show that we are able to model long sequences with musical structure for both individual instruments and a three-piece band (lead, bass, and drums). Finally, we demonstrate the effectiveness of scheduled sampling in significantly improving our reconstruction accuracy.

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