LEADER 03621nam  22005055  450 
001 9910299873703321
005 20200706130123.0
010   $a3-319-67020-4
024 7 $a10.1007/978-3-319-67020-1
035   $a(CKB)3780000000451350
035   $a(DE-He213)978-3-319-67020-1
035   $a(MiAaPQ)EBC5015479
035   $a(PPN)204534941
035   $a(EXLCZ)993780000000451350
100   $a20170901d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aCanonical Correlation Analysis in Speech Enhancement /$fby Jacob Benesty, Israel Cohen
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (IX, 121 p. 47 illus. in color.) 
225 1 $aSpringerBriefs in Electrical and Computer Engineering,$x2191-8112
311   $a3-319-67019-0 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- 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.
330   $aThis book focuses on the application of canonical correlation analysis (CCA) to speech enhancement using the filtering approach. The authors explain how to derive different classes of time-domain and time-frequency-domain noise reduction filters, which are optimal from the CCA perspective for both single-channel and multichannel speech enhancement. Enhancement of noisy speech has been a challenging problem for many researchers over the past few decades and remains an active research area. Typically, speech enhancement algorithms operate in the short-time Fourier transform (STFT) domain, where the clean speech spectral coefficients are estimated using a multiplicative gain function. A filtering approach, which can be performed in the time domain or in the subband domain, obtains an estimate of the clean speech sample at every time instant or time-frequency bin by applying a filtering vector to the noisy speech vector. Compared to the multiplicative gain approach, the filtering approach more naturally takes into account the correlation of the speech signal in adjacent time frames. In this study, the authors pursue the filtering approach and show how to apply CCA to the speech enhancement problem. They also address the problem of adaptive beamforming from the CCA perspective, and show that the well-known Wiener and minimum variance distortionless response (MVDR) beamformers are particular cases of a general class of CCA-based adaptive beamformers.
410  0$aSpringerBriefs in Electrical and Computer Engineering,$x2191-8112
606   $aSignal processing
606   $aImage processing
606   $aSpeech processing systems
606   $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051
615  0$aSignal processing.
615  0$aImage processing.
615  0$aSpeech processing systems.
615 14$aSignal, Image and Speech Processing.
676   $a621.382
700   $aBenesty$b Jacob$4aut$4http://id.loc.gov/vocabulary/relators/aut$0721063
702   $aCohen$b Israel$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910299873703321
996   $aCanonical Correlation Analysis in Speech Enhancement$92501289
997   $aUNINA