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Deep Learning Based Phase Reconstruction for Speaker Separation: A Trigonometric Perspective

Authors :
DeLiang Wang
Ke Tan
Zhong-Qiu Wang
Source :
ICASSP
Publication Year :
2019
Publisher :
IEEE, 2019.

Abstract

This study investigates phase reconstruction for deep learning based monaural talker-independent speaker separation in the short-time Fourier transform (STFT) domain. The key observation is that, for a mixture of two sources, with their magnitudes accurately estimated and under a geometric constraint, the absolute phase difference between each source and the mixture can be uniquely determined; in addition, the source phases at each time-frequency (T-F) unit can be narrowed down to only two candidates. To pick the right candidate, we propose three algorithms based on iterative phase reconstruction, group delay estimation, and phase-difference sign prediction. State-of-the-art results are obtained on the publicly available wsj0-2mix and 3mix corpus.<br />Comment: 5 pages, in submission to ICASSP-2019

Details

Database :
OpenAIRE
Journal :
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Accession number :
edsair.doi.dedup.....bcb6b6793fb5fd3329fdb2def44c319f