Canonical Correlation Analysis in Speech Enhancement

Autor: Jacob Benesty, Israel Cohen

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Editura: Springer International Publishing AG

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This book focuses on the application of canonical correlation analysis (CCA) to speech enhancement u...

Informații despre carte

Autor

Jacob Benesty, Israel Cohen

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2017

Pagini

121

EAN

9783319670195

ISBN

3319670190

Enbook ID

16830561

Editura

Springer International Publishing AG

Greutate

2117

Dimensiuni

155 x 235 x 9

Descriere completă

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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Canonical Correlation Analysis in Speech Enhancement

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