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Canonical correlation analysis in sp...

Benesty, Jacob.

Canonical correlation analysis in speech enhancement

Record Type:	Electronic resources : Monograph/item
Title/Author:	Canonical correlation analysis in speech enhancementby Jacob Benesty, Israel Cohen.
Author:	Benesty, Jacob.
other author:	Cohen, Israel.
Published:	Cham :Springer International Publishing :2018.
Description:	ix, 121 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Speech processing systems.
Online resource:	http://dx.doi.org/10.1007/978-3-319-67020-1
ISBN:	9783319670201$q(electronic bk.)

Canonical correlation analysis in speech enhancement
Benesty, Jacob.

Canonical correlation analysis in speech enhancement[electronic resource] /by Jacob Benesty, Israel Cohen. - Cham :Springer International Publishing :2018. - ix, 121 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering,2191-8112. - SpringerBriefs in electrical and computer engineering..

Introduction -- Canonical Correlation Analysis -- Single-Channel Speech Enhancement in the Time Domain -- Single-Channel Speech Enhancement in the STFT Domain -- Multichannel Speech Enhancement in the Time Domain -- Multichannel Speech Enhancement in the Time Domain -- Adaptive Beamforming.

This book focuses on the application of canonical correlation analysis (CCA) to speech enhancement using the filtering approach. The authors explain how to derive different classes of time-domain and time-frequency-domain noise reduction filters, which are optimal from the CCA perspective for both single-channel and multichannel speech enhancement. Enhancement of noisy speech has been a challenging problem for many researchers over the past few decades and remains an active research area. Typically, speech enhancement algorithms operate in the short-time Fourier transform (STFT) domain, where the clean speech spectral coefficients are estimated using a multiplicative gain function. A filtering approach, which can be performed in the time domain or in the subband domain, obtains an estimate of the clean speech sample at every time instant or time-frequency bin by applying a filtering vector to the noisy speech vector. Compared to the multiplicative gain approach, the filtering approach more naturally takes into account the correlation of the speech signal in adjacent time frames. In this study, the authors pursue the filtering approach and show how to apply CCA to the speech enhancement problem. They also address the problem of adaptive beamforming from the CCA perspective, and show that the well-known Wiener and minimum variance distortionless response (MVDR) beamformers are particular cases of a general class of CCA-based adaptive beamformers.

ISBN: 9783319670201$q(electronic bk.)

Standard No.: 10.1007/978-3-319-67020-1doiSubjects--Topical Terms:

184755
Speech processing systems.

LC Class. No.: TK7882.S65

Dewey Class. No.: 006.454

Canonical correlation analysis in speech enhancement
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245 1 0 $a Canonical correlation analysis in speech enhancement $h [electronic resource] / $c by Jacob Benesty, Israel Cohen.
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490 1 $a SpringerBriefs in electrical and computer engineering, $x 2191-8112
505 0 $a Introduction -- Canonical Correlation Analysis -- Single-Channel Speech Enhancement in the Time Domain -- Single-Channel Speech Enhancement in the STFT Domain -- Multichannel Speech Enhancement in the Time Domain -- Multichannel Speech Enhancement in the Time Domain -- Adaptive Beamforming.
520 $a This book focuses on the application of canonical correlation analysis (CCA) to speech enhancement using the filtering approach. The authors explain how to derive different classes of time-domain and time-frequency-domain noise reduction filters, which are optimal from the CCA perspective for both single-channel and multichannel speech enhancement. Enhancement of noisy speech has been a challenging problem for many researchers over the past few decades and remains an active research area. Typically, speech enhancement algorithms operate in the short-time Fourier transform (STFT) domain, where the clean speech spectral coefficients are estimated using a multiplicative gain function. A filtering approach, which can be performed in the time domain or in the subband domain, obtains an estimate of the clean speech sample at every time instant or time-frequency bin by applying a filtering vector to the noisy speech vector. Compared to the multiplicative gain approach, the filtering approach more naturally takes into account the correlation of the speech signal in adjacent time frames. In this study, the authors pursue the filtering approach and show how to apply CCA to the speech enhancement problem. They also address the problem of adaptive beamforming from the CCA perspective, and show that the well-known Wiener and minimum variance distortionless response (MVDR) beamformers are particular cases of a general class of CCA-based adaptive beamformers.
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