A Universal Music Translation Network

May 5, 2019

Abstract

We present a method for translating music across musical instruments and styles. This method is based on unsupervised training of a multi-domain wavenet autoencoder, with a shared encoder and a domain-independent latent space that is trained end-to-end on waveforms. Employing a diverse training dataset and large net capacity, the single encoder allows us to translate also from musical domains that were not seen during training. We evaluate our method on a dataset collected from professional musicians, and achieve convincing translations. We also study the properties of the obtained translation and demonstrate translating even from a whistle, potentially enabling the creation of instrumental music by untrained humans.

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AUTHORS

Written by

Noam Mor

Lior Wolf

Adam Polyak

Yaniv Taigman

Publisher

International Conference on Learning Representations (ICLR)

Research Topics

Speech & Audio

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