  We present a method for translating music across musical instruments, genres,
and styles. This method is based on a multi-domain wavenet autoencoder, with a
shared encoder and a disentangled latent space that is trained end-to-end on
waveforms. Employing a diverse training dataset and large net capacity, the
domain-independent encoder allows us to translate even from musical domains
that were not seen during training. The method is unsupervised and does not
rely on supervision in the form of matched samples between domains or musical
transcriptions. We evaluate our method on NSynth, as well as on a dataset
collected from professional musicians, and achieve convincing translations,
even when translating from whistling, potentially enabling the creation of
instrumental music by untrained humans.
