A Universal Music Translation Network

Noam Mor, Lior Wolf, Adam Polyak, Yaniv Taigman

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.

TL;DR: An automatic method for converting music between instruments and styles

Data: [MusicNet](https://paperswithcode.com/dataset/musicnet)

Community Implementations: [ 5 code implementations](https://www.catalyzex.com/paper/arxiv:1805.07848/code)