Emergent Mind
End-to-End Sound Source Separation Conditioned On Instrument Labels
(1811.01850)
Published Nov 5, 2018
in
cs.SD
,
cs.LG
,
and
eess.AS
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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