Steer OpenAI's Jukebox with Music Taggers!
The closest thing we have to VQGAN+CLIP for music!
Read the paper here. Submitted to ICASSP 2022.
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.
Click here to see our Github repository.
Run it yourself Colab notebook here:
Audio examples are not displayed on https://github.com/ethman/tagbox, please
click here to see the demo page.
TagBox excels in separating prominent melodies from within sparse mixtures.
Mixture
TagBox Output
| hyperparam | setting |
|---|---|
| fft size(s) | 512, 1024, 2048 |
| lr | 10.0 |
| steps | 200 |
| tagger model(s) | fcn, hcnn, musicnn |
| tagger data | MTAT |
| selected tags | All vocal tags |
Mixture
TagBox Output
| hyperparam | setting |
|---|---|
| fft size(s) | 512, 1024, 2048 |
| lr | 10.0 |
| steps | 100 |
| tagger model(s) | fcn, hcnn, musicnn |
| tagger data | MTG-Jamendo |
| selected tags | Violin |
Mixture
TagBox Output
| hyperparam | setting |
|---|---|
| fft size(s) | 512, 1024, 2048 |
| lr | 5.0 |
| steps | 200 |
| tagger model(s) | fcn, hcnn |
| tagger data | MTAT |
| selected tags | All vocal tags |
Similar to multi-scale spectral losses, when we use masks with multiple FFT sizes we notice that the quality of the output increases.
Mixture
TagBox with fft_size=[1024]
Notice the warbling effects in the following example:
TagBox with fft_size=[1024, 2048]
Those warbling effects are mitigated by using two fft sizes:
These results, however, are not reflected in the SDR evaluation metrics.
Remove the masking step enables Jukebox to generate any audio that will optimize the tag. In some situations, TagBox will pick out the melody and resynthesize it. But it adds lots of artifacts, making it sound like the audio was recorded in a snowstorm.
Mixture
"Style Transfer"
Here, we optimize the "guitar" tag without the mask. Notice that the "All it
says to you" melody sounds like a guitar being plucked in a snowstorm:
If you use this your academic research, please cite the following:
@misc{manilow2021unsupervised,
title={Unsupervised Source Separation By Steering Pretrained Music Models},
author={Ethan Manilow and Patrick O'Reilly and Prem Seetharaman and Bryan Pardo},
year={2021},
eprint={2110.13071},
archivePrefix={arXiv},
primaryClass={cs.SD}
}