Multimicrophone MMSE-Based Speech Source Separation

Shmulik Markovich-Golan; Israel Cohen; Sharon Gannot

doi:10.1007/978-3-319-73031-8_12

Multimicrophone MMSE-Based Speech Source Separation

Shmulik Markovich-Golan, Israel Cohen, Sharon Gannot

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Beamforming methods using a microphone array successfully utilize spatial diversity for speech separation and noise reduction. Adaptive design of the beamformer based on various minimum mean squared error (MMSE) criteria significantly improves performance compared to fixed, and data-independent design. These criteria differ in their considerations to noise minimization and desired speech distortion. Three common data-dependent beamformers, namely, matched filter (MF), MWF and LCMV are presented and analyzed. Estimation methods for implementing the various beamformers are surveyed. Simple examples of applying the various beamformers to simulated narrowband signals in an anechoic environment and to speech signals in a real-life reverberant environment are presented and discussed.

Original language	English
Title of host publication	AUDIO SOURCE SEPARATION
Pages	301-331
Number of pages	31
DOIs	https://doi.org/10.1007/978-3-319-73031-8_12
State	Published - 2018

Publication series

Name	Signals and Communication Technology

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications
Electrical and Electronic Engineering

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10.1007/978-3-319-73031-8_12

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abstract = "Beamforming methods using a microphone array successfully utilize spatial diversity for speech separation and noise reduction. Adaptive design of the beamformer based on various minimum mean squared error (MMSE) criteria significantly improves performance compared to fixed, and data-independent design. These criteria differ in their considerations to noise minimization and desired speech distortion. Three common data-dependent beamformers, namely, matched filter (MF), MWF and LCMV are presented and analyzed. Estimation methods for implementing the various beamformers are surveyed. Simple examples of applying the various beamformers to simulated narrowband signals in an anechoic environment and to speech signals in a real-life reverberant environment are presented and discussed.",

author = "Shmulik Markovich-Golan and Israel Cohen and Sharon Gannot",

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Multimicrophone MMSE-Based Speech Source Separation

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