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Transfer Learning with Jukebox for Music Source Separation. (arXiv:2111.14200v3 [eess.AS] UPDATED)
Sept. 22, 2022, 1:12 a.m. | W. Zai El Amri, O. Tautz, H. Ritter, A. Melnik
cs.LG updates on arXiv.org arxiv.org
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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