Canonical correlation analysis in speech enhancement /
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| Author / Creator: | Benesty, Jacob, author. |
|---|---|
| Imprint: | Cham, Switzerland : Springer, [2018] ©2018 |
| Description: | 1 online resource |
| Language: | English |
| Series: | SpringerBriefs in electrical and computer engineering, 2191-8112 SpringerBriefs in electrical and computer engineering. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11541595 |
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| 100 | 1 | |a Benesty, Jacob, |e author. |0 http://id.loc.gov/authorities/names/n00001625 | |
| 245 | 1 | 0 | |a Canonical correlation analysis in speech enhancement / |c Jacob Benesty, Israel Cohen. |
| 264 | 1 | |a Cham, Switzerland : |b Springer, |c [2018] | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a SpringerBriefs in electrical and computer engineering, |x 2191-8112 | |
| 505 | 0 | |a Abstract; 1 Introduction; 1.1 Canonical Correlation Analysis and Speech Enhancement; 1.2 Organization of the Work; References; 2 Canonical Correlation Analysis; 2.1 Preliminaries; 2.2 How CCA Works; 2.3 The Singular Case; References; 3 Single-Channel Speech Enhancement in the Time Domain; 3.1 Signal Model and Problem Formulation; 3.2 Canonical Linear Filtering; 3.3 Performance Measures; 3.4 Optimal Canonical Filters from the Desired and Noisy Signals; 3.5 Optimal Canonical Filters from the Noise and Noisy Signals; References; 4 Single-Channel Speech Enhancement in the STFT Domain. | |
| 505 | 8 | |a 4.1 Signal Model and Problem Formulation4.2 Canonical Linear Filtering; 4.3 Performance Measures; 4.4 Optimal Canonical Filters from the Desired and Noisy Signals; 4.5 Optimal Canonical Filters from the Noise and Noisy Signals; References; 5 Multichannel Speech Enhancement in the Time Domain; 5.1 Signal Model and Problem Formulation; 5.2 Canonical Linear Filtering; 5.3 Performance Measures; 5.4 Optimal Canonical Filters from the Desired and Noisy Signals; 5.5 Optimal Canonical Filters from the Noise and Noisy Signals; 5.6 Other Possibilities; References. | |
| 505 | 8 | |a 6 Multichannel Speech Enhancement in the STFT Domain6.1 Signal Model and Problem Formulation; 6.2 Canonical Linear Filtering; 6.3 Performance Measures; 6.4 Optimal Canonical Filters from the Desired and Noisy Signals; 6.5 Optimal Canonical Filters from the Noise and Noisy Signals; 6.6 Other Possibilities; References; 7 Adaptive Beamforming; 7.1 Signal Model and Problem Formulation; 7.2 Canonical Linear Filtering; 7.3 Performance Measures; 7.4 Optimal Canonical Filters from the Desired and Noisy Signals; 7.5 Optimal Canonical Filters from the Noise and Noisy Signals; References; Index. | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 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. | ||
| 650 | 0 | |a Speech processing systems. |0 http://id.loc.gov/authorities/subjects/sh85126450 | |
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| 650 | 7 | |a Speech processing systems. |2 fast |0 (OCoLC)fst01129243 | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Cohen, Israel, |d 1879-1961, |e author. | |
| 776 | 0 | 8 | |i Print version: |z 3319670190 |z 9783319670195 |w (OCoLC)996418313 |
| 830 | 0 | |a SpringerBriefs in electrical and computer engineering. |0 http://id.loc.gov/authorities/names/no2011108018 | |
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