000 03821nam a22005295i 4500
001 978-3-031-02559-4
003 DE-He213
005 20240730164733.0
007 cr nn 008mamaa
008 220601s2010 sz | s |||| 0|eng d
020 _a9783031025594
_9978-3-031-02559-4
024 7 _a10.1007/978-3-031-02559-4
_2doi
050 4 _aTK1-9971
072 7 _aTHR
_2bicssc
072 7 _aTEC007000
_2bisacsh
072 7 _aTHR
_2thema
082 0 4 _a621.3
_223
100 1 _aPaleologu, Constantin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985870
245 1 0 _aSparse Adaptive Filters for Echo Cancellation
_h[electronic resource] /
_cby Constantin Paleologu, Jacob Benesty, Silviu Ciochina.
250 _a1st ed. 2010.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2010.
300 _aIX, 114 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aElectrical engineering.
_985872
650 0 _aSignal processing.
_94052
650 0 _aAcoustical engineering.
_99499
650 1 4 _aElectrical and Electronic Engineering.
_985875
650 2 4 _aSignal, Speech and Image Processing.
_931566
650 2 4 _aEngineering Acoustics.
_931982
700 1 _aBenesty, Jacob.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985876
700 1 _aCiochina, Silviu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985877
710 2 _aSpringerLink (Online service)
_985879
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031014314
776 0 8 _iPrinted edition:
_z9783031036873
830 0 _aSynthesis Lectures on Speech and Audio Processing,
_x1932-1678
_985880
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02559-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c85871
_d85871