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003			ES-MaUEC
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007			cr nn 008mamaa
008			230511s2010 sz | s |||| 0|eng d
020			_a9783031025594
024	7		_a10.1007/978-3-031-02559-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5102.98 _b2010 EB
100	1		_aPaleologu, Constantin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688479
245	1	0	_aSparse Adaptive Filters for Echo Cancellation _cby Constantin Paleologu, Jacob Benesty, Silviu Ciochina
250			_a1st edition 2010
264		1	_aCham _bSpringer International Publishing _c2010
300			_a1 recurso en línea (IX, 114 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 -- Sparseness Measures -- Performance Measures -- Wiener and Basic Adaptive Filters -- Basic Proportionate-Type NLMS Adaptive Filters -- The Exponentiated Gradient Algorithms -- The Mu-Law PNLMS and Other PNLMS-Type Algorithms -- Variable Step-Size PNLMS Algorithms -- Proportionate Affine Projection Algorithms -- Experimental Study.
520			_aAdaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellation. Besides a comprehensive review of the basic proportionate-type algorithms, we also present some of the latest developments in the field and propose some new solutions for further performance improvement, e.g., variable step-size versions and novel proportionate-type affine projection algorithms. An experimental study is also provided in order to compare many sparse adaptive filters in different echo cancellation scenarios. Table of Contents: Introduction / Sparseness Measures / Performance Measures / Wiener and Basic Adaptive Filters / Basic Proportionate-Type NLMS Adaptive Filters / The Exponentiated Gradient Algorithms / The Mu-Law PNLMS and Other PNLMS-Type Algorithms / Variable Step-Size PNLMS Algorithms / Proportionate Affine Projection Algorithms / Experimental Study.
988			_aSynthesis Collection of Technology_2010
650		7	_2embne _9162764 _aProcesadores digitales de señal
700	1		_aBenesty, Jacob _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673239
700	1		_aCiochină, Silviu _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688480
776	0	8	_iPrinted edition: _z9783031014314
776	0	8	_iPrinted edition: _z9783031036873
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02559-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eIG _zSI