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.
BibTeX
@inproceedings{
mor2018autoencoderbased,
title={Autoencoder-based Music Translation},
author={Noam Mor and Lior Wolf and Adam Polyak and Yaniv Taigman},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=HJGkisCcKm},
}