  We showcase an unsupervised method that repurposes deep models trained for
music generation and music tagging for audio source separation, without any
retraining. An audio generation model is conditioned on an input mixture,
producing a latent encoding of the audio used to generate audio. This generated
audio is fed to a pretrained music tagger that creates source labels. The
cross-entropy loss between the tag distribution for the generated audio and a
predefined distribution for an isolated source is used to guide gradient ascent
in the (unchanging) latent space of the generative model. This system does not
update the weights of the generative model or the tagger, and only relies on
moving through the generative model's latent space to produce separated
sources. We use OpenAI's Jukebox as the pretrained generative model, and we
couple it with four kinds of pretrained music taggers (two architectures and
two tagging datasets). Experimental results on two source separation datasets,
show this approach can produce separation estimates for a wider variety of
sources than any tested supervised or unsupervised system. This work points to
the vast and heretofore untapped potential of large pretrained music models for
audio-to-audio tasks like source separation.
