000			04196cam a2200589Ia 4500
001			u380077
003			SIRSI
005			20160812084629.0
006			m o d
007			cr unu||||||||
008			140218s2014 enka ob 001 0 eng d
040			_aUMI _beng _cUMI _dIDEBK _dOPELS _dE7B _dYDXCP _dCOO _dDEBBG _dDEBSZ _dGGVRL _dCDX
019			_a868285275
020			_a1306315131 (electronic bk.)
020			_a9781306315135 (electronic bk.)
020			_a9780128002537 (electronic bk.)
020			_a0128002530 (electronic bk.)
020			_z9780128001394
020			_a0128001399
020			_a9780128001394
029	1		_aDEBSZ _b404338461
029	1		_aDEBSZ _b414271904
029	1		_aCHVBK _b327777354
029	1		_aCHBIS _b010295203
050		4	_aTK7882.S65 _bS744 2014
082	0	4	_a006.4/5 _223
049			_aTEFA
245	0	0	_aSpeech enhancement _h[recurso electrónico] : _ba signal subspace perspective / _cJacob Benesty ... [et al.].
250			_a1st ed.
260			_aOxford ; _aWaltham, MA : _bAcademic Press, _c2014.
300			_a1 online resource (1 v.) : _bill.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
504			_aIncludes bibliographical references and index.
588			_aDescription based on online resource; title from title page (Safari, viewed Feb. 6, 2014).
520			_aSpeech enhancement is a classical problem in signal processing, yet still largely unsolved. Two of the conventional approaches for solving this problem are linear filtering, like the classical Wiener filter, and subspace methods. These approaches have traditionally been treated as different classes of methods and have been introduced in somewhat different contexts. Linear filtering methods originate in stochastic processes, while subspace methods have largely been based on developments in numerical linear algebra and matrix approximation theory. This book bridges the gap between these two classes of methods by showing how the ideas behind subspace methods can be incorporated into traditional linear filtering. In the context of subspace methods, the enhancement problem can then be seen as a classical linear filter design problem. This means that various solutions can more easily be compared and their performance bounded and assessed in terms of noise reduction and speech distortion. The book shows how various filter designs can be obtained in this framework, including the maximum SNR, Wiener, LCMV, and MVDR filters, and how these can be applied in various contexts, like in single-channel and multichannel speech enhancement, and in both the time and frequency domains. First short book treating subspace approaches in a unified way for time and frequency domains, single-channel, multichannel, as well as binaural, speech enhancement. Bridges the gap between optimal filtering methods and subspace approaches.Includes original presentation of subspace methods from different perspectives.
505	0		_aChapter 1. Introduction -- chapter 2. General concept with the diagonalization of the speech correlation matrix -- chapter 3. General concept with the joint diagonalization of the speech and noise correlation matrices -- chapter 4. Single-channel speech enhancement in the time domain -- chapter 5. Multichannel speech enhancement in the time domain -- chapter 6. Multichannel speech enhancement in the frequency domain -- chapter 7. A Bayesian approach to the speech subspace estimation -- chapter 8. Evaluation of the time-domain speech enhancement filters.
650		0	_aSpeech processing systems.
650		0	_aSignal processing.
650		7	_aSpeech processing systems. _2local
650		7	_aSignal processing. _2local
655		4	_aElectronic books.
655		0	_aElectronic books.
700	1		_aBenesty, Jacob.
776	0	8	_iPrint version: _z9781306315135
856	4	0	_zLibro electrónico _3ScienceDirect _uhttp://148.231.10.114:2048/login?url=http://www.sciencedirect.com/science/book/9780128001394
596			_a19
942			_cLIBRO_ELEC
999			_c206998 _d206998