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008			220601s2011 sz | o |||| 0|eng d
020			_a9783031025617
024	7		_a10.1007/978-3-031-02561-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7882.S65 _b2011 EB
100	1		_aBenesty, Jacob _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673239
245	1	2	_aA Perspective on Single-Channel Frequency-Domain Speech Enhancement _cby Jacob Benesty, Yiteng Huang
250			_a1st edition 2011
264		1	_aCham _bSpringer International Publishing _c2011
300			_a1 recurso en línea (VIII, 101 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Speech and Audio Processing _x1932-1678
505	0		_aIntroduction -- Problem Formulation -- Performance Measures -- Linear and Widely Linear Models -- Optimal Filters with Model 1 -- Optimal Filters with Model 2 -- Optimal Filters with Model 3 -- Optimal Filters with Model 4 -- Experimental Study.
520			_aThis book focuses on a class of single-channel noise reduction methods that are performed in the frequency domain via the short-time Fourier transform (STFT). The simplicity and relative effectiveness of this class of approaches make them the dominant choice in practical systems. Even though many popular algorithms have been proposed through more than four decades of continuous research, there are a number of critical areas where our understanding and capabilities still remain quite rudimentary, especially with respect to the relationship between noise reduction and speech distortion. All existing frequency-domain algorithms, no matter how they are developed, have one feature in common: the solution is eventually expressed as a gain function applied to the STFT of the noisy signal only in the current frame. As a result, the narrowband signal-to-noise ratio (SNR) cannot be improved, and any gains achieved in noise reduction on the fullband basis come with a price to pay, which is speech distortion. In this book, we present a new perspective on the problem by exploiting the difference between speech and typical noise in circularity and interframe self-correlation, which were ignored in the past. By gathering the STFT of the microphone signal of the current frame, its complex conjugate, and the STFTs in the previous frames, we construct several new, multiple-observation signal models similar to a microphone array system: there are multiple noisy speech observations, and their speech components are correlated but not completely coherent while their noise components are presumably uncorrelated. Therefore, the multichannel Wiener filter and the minimum variance distortionless response (MVDR) filter that were usually associated with microphone arrays will be developed for single-channel noise reduction in this book. This might instigate a paradigm shift geared toward speech distortionless noise reduction techniques. Table of Contents: Introduction / Problem Formulation / Performance Measures / Linear and Widely Linear Models / Optimal Filters with Model 1 / Optimal Filters with Model 2 / Optimal Filters with Model 3 / Optimal Filters with Model 4 / Experimental Study.
988			_aSynthesis Collection of Technology_2011
650		7	_2embne _9147323 _aReconocimiento automático del lenguaje
650		7	_2embne _9168751 _aRuido _xControl
700	1		_aHuang, Yiteng _d1972- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686496
776	0	8	_iPrinted edition: _z9783031014338
776	0	8	_iPrinted edition: _z9783031036897
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02561-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eb _zSI