Transfer Learning with Jukebox for Music Source Separation
2021-11-28Code Available1· sign in to hype
W. Zai El Amri, O. Tautz, H. Ritter, A. Melnik
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ReproduceCode
- github.com/wzaielamri/unmixOfficialIn paperpytorch★ 36
Abstract
In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for the problem of audio source separation from a single mixed audio channel. Our neural network architecture, which is using transfer learning, is quick to train and the results demonstrate performance comparable to other state-of-the-art approaches that require a lot more compute resources, training data, and time. We provide an open-source code implementation of our architecture (https://github.com/wzaielamri/unmix)
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| MUSDB18-HQ | Unmix | SDR (avg) | 4.19 | — | Unverified |