Hierarchical Variational Autoencoders for Music
Abstract
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.
Research Areas
