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Canonical correlation analysis in sp...

Benesty, Jacob.

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Canonical correlation analysis in speech enhancement

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Canonical correlation analysis in speech enhancement/ by Jacob Benesty, Israel Cohen.
作者:	Benesty, Jacob.
其他作者:	Cohen, Israel.
出版者:	Cham :Springer International Publishing : : 2018.,
面頁冊數:	ix, 121 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Speech processing systems. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-67020-1
ISBN:	9783319670201

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

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

590570
Speech processing systems.

LC Class. No.: TK7882.S65

Dewey Class. No.: 006.454

Canonical correlation analysis in speech enhancement
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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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