LCMV beamformer with DNN-based multichannel concurrent speakers detector

Shlomo E. Chazan, Jacob Goldberger, Sharon Gannot

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

2 Scopus citations

Abstract

Application of the linearly constrained minimum variance (LCMV) beamformer (BF) to speaker extraction tasks in real-life scenarios necessitates a sophisticated control mechanism to facilitate the estimation of the noise spatial cross-power spectral density (cPSD) matrix and the relative transfer function (RTF) of all sources of interest. We propose a deep neural network (DNN)-based multichannel concurrent speakers detector (MCCSD) that utilizes all available microphone signals to detect the activity patterns of all speakers. Time frames classified as no active speaker frames will be utilized to estimate the cPSD, while time frames with a single detected speaker will be utilized for estimating the associated RTF. No estimation will take place during concurrent speaker activity. Experimental results show that the multi-channel approach significantly improves its single-channel counterpart.

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference, EUSIPCO 2018
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1562-1566
Number of pages5
ISBN (Electronic)9789082797015
DOIs
StatePublished - 29 Nov 2018
Event26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
Duration: 3 Sep 20187 Sep 2018

Publication series

NameEuropean Signal Processing Conference
Volume2018-September
ISSN (Print)2219-5491

Conference

Conference26th European Signal Processing Conference, EUSIPCO 2018
Country/TerritoryItaly
CityRome
Period3/09/187/09/18

Bibliographical note

Publisher Copyright:
© EURASIP 2018.

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