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arXiv · 1811.01850

End-to-End Sound Source Separation Conditioned On Instrument Labels

Olga Slizovskaia·Leo Kim·Gloria Haro·Emilia Gomez
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Abstract

Can we perform an end-to-end music source separation with a variable number of sources using a deep learning model? We present an extension of the Wave-U-Net model which allows end-to-end monaural source separation with a non-fixed number of sources. Furthermore, we propose multiplicative conditioning with instrument labels at the bottleneck of the Wave-U-Net and show its effect on the separation results. This approach leads to other types of conditioning such as audio-visual source separation and score-informed source separation.

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BibTeXRIS

Olga Slizovskaia, Leo Kim, Gloria Haro, Emilia Gomez. 2018-11-05. End-to-End Sound Source Separation Conditioned On Instrument Labels. https://arxiv.org/abs/1811.01850

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