000			03475nam a22004095i 4500
999			_c102686 _d102686 _x1
001			102686
003			DE-He213
005			20230207040633.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			170901s2018 gw | s |||| 0|eng d
020			_a9783319670201
024	7		_a10.1007/978-3-319-67020-1 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7882.S65 _bB464 2018 EB
100	1		_aBenesty, Jacob _eautor. _0http://id.loc.gov/authorities/names/n00001625 _1http://viaf.org/viaf/85410897/ _9673239
245	1	0	_aCanonical Correlation Analysis in Speech Enhancement _cby Jacob Benesty, Israel Cohen.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (IX, 121 páginas 47 ilustraciones a color.)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
490	0		_aEngineering (Springer-11647)
505	0		_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.
520	3		_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.
988			_aEBSPRINGER_2018
650		7	_2embne _9147323 _aReconocimiento automático del lenguaje
700	1		_aCohen, Israel. _eautor. _0http://id.loc.gov/authorities/names/n00046952 _1http://viaf.org/viaf/100921459/ _1http://dbpedia.org/resource/Israel_Cohen
776	0	8	_iEdición impresa: _z9783319670195
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://dx.doi.org/10.1007/978-3-319-67020-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE