18th AIAI 2022, 17 - 20 June 2022, Greece

Transfer Learning with Jukebox for Music Source Separation

Wadhah Zai El Amri, Oliver Tautz, Helge Ritter, Andrew Melnik


  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)  

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