Decoupling magnitude and phase estimation with deep resunet for music source separation

Qiuqiang Kong, Yin Cao, Haohe Liu, Keunwoo Choi, Yuxuan Wang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

31 Citations (Scopus)

Abstract

Deep neural network based methods have been successfully applied to music source separation. They typically learn a mapping from a mixture spectrogram to a set of source spectrograms, all with magnitudes only. This approach has several limitations: 1) its incorrect phase reconstruction degrades the performance, 2) it limits the magnitude of masks between 0 and 1 while we observe that 22% of time-frequency bins have ideal ratio mask values of over 1 in a popular dataset, MUSDB18, 3) its potential on very deep architectures is under-explored. Our proposed system is designed to overcome these. First, we propose to estimate phases by estimating complex ideal ratio masks (cIRMs) where we decouple the estimation of cIRMs into magnitude and phase estimations. Second, we extend the separation method to effectively allow the magnitude of the mask to be larger than 1. Finally, we propose a residual UNet architecture with up to 143 layers. Our proposed system achieves a state-of-the-art MSS result on the MUSDB18 dataset, especially, a SDR of 8.98 dB on vocals, outperforming the previous best performance of 7.24 dB. The source code is available at: https://github.com/bytedance/ music_source_separation.

Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Music Information Retrieval, ISMIR 2021
PublisherInternational Society for Music Information Retrieval
Pages342-349
Number of pages8
ISBN (Electronic)9781732729902
Publication statusPublished - 2021
Externally publishedYes
Event22nd International Conference on Music Information Retrieval, ISMIR 2021 - Virtual, Online
Duration: 7 Nov 202112 Nov 2021

Publication series

NameProceedings of the 22nd International Conference on Music Information Retrieval, ISMIR 2021

Conference

Conference22nd International Conference on Music Information Retrieval, ISMIR 2021
CityVirtual, Online
Period7/11/2112/11/21

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