All for One and One for All: Improving Music Separation by Bridging Networks

Ryosuke Sawata,Stefan Uhlich,Shusuke Takahashi,Yuki Mitsufuji

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)（2021）

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摘要

This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes. First, by using MDL we take advantage of the frequency and time domain representation of audio signals. Next, we utilize the relationship among instruments by jointly considering them. We do this on the one hand by modifying the network archi...

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关键词

Music source separation (MSS),Deep neural network (DNN),Loss function

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