000 03746nam a22005295i 4500
999 _c13546
_d13546
001 978-3-319-67020-1
003 DE-He213
005 20211129141953.0
008 170901s2018 gw | s |||| 0|eng d
020 _a9783319670201
040 _cAIKTC-KRRC
041 _aENG
072 7 _aTTBM
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aTTBM
_2thema
072 7 _aUYS
_2thema
082 0 4 _a621.382
_223
100 1 _aBenesty, Jacob.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aCanonical Correlation Analysis in Speech Enhancement
_h[electronic resource] /
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aIX, 121 p. 47 illus. in color.
_bCard Paper
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Electrical and Computer Engineering,
_x2191-8112
520 _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.
650 0 _aEXTC Engineering
_94619
653 _aSignal, Image and Speech Processing.
700 1 _aCohen, Israel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319670195
776 0 8 _iPrinted edition:
_z9783319670218
830 0 _aSpringerBriefs in Electrical and Computer Engineering,
_x2191-8112
856 4 0 _uhttps://doi.org/10.1007/978-3-319-67020-1
_zClick here to access eBook in Springer Nature platform. (Within Campus only.)
942 _cEBOOKS
_2ddc