000 05595nam a2200781 i 4500
001 6813553
003 IEEE
005 20220822104803.0
006 m eo d
007 cr cn |||m|||a
008 100604s2010 caua foab 001 0 eng d
020 _a9781598293074 (electronic bk.)
020 _z9781598293067 (pbk.)
024 7 _a10.2200/S00289ED1V01Y201006SAP006
_2doi
035 _a(CaBNVSL)gtp00540743
035 _a(OCoLC)647985778
040 _aCaBNVSL
_cCaBNVSL
_dCaBNVSL
050 4 _aTK7872.F5
_bP257 2010
082 0 4 _a621.3815324
_222
100 1 _aPaleologu, Constantin.
245 1 0 _aSparse adaptive filters for echo cancellation
_h[electronic resource] /
_cConstantin Paleologu, Jacob Benesty, Silviu Ciochină.
260 _aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :
_bMorgan & Claypool,
_cc2010.
300 _a1 electronic text (ix, 114 p. : ill.) :
_bdigital file.
490 1 _aSynthesis lectures on speech and audio processing,
_x1932-1678 ;
_v# 6
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
500 _aSeries from website.
504 _aIncludes bibliographical references (p. 103-109) and index.
505 0 _a1. Introduction -- Echo Cancellation -- Double-Talk Detection -- Sparse Adaptive Filters -- Notation -- Sparseness Measures -- Vector Norms -- Examples of Impulse Responses --
505 0 _a2. Sparseness Measure Based on the L0 Norm -- Sparseness Measure Based on the L1 and L2 Norms -- Sparseness Measure Based on the L1 and L[infinity] Norms -- Sparseness Measure Based on the L2 and L[infinity] Norms --
505 0 _a3. Performance Measures -- Mean-Square Error -- Echo-Return Loss Enhancement -- Misalignment --
505 0 _a4. Wiener and Basic Adaptive Filters -- Wiener Filter -- Efficient Computation of the Wiener-Hopf Equations -- Deterministic Algorithm -- Stochastic Algorithm -- Variable Step-Size NLMS Algorithm -- Convergence of the Misalignment -- Sign Algorithms --
505 0 _a5. Basic Proportionate-Type NLMS Adaptive Filters -- General Derivation -- The Proportionate NLMS (PNLMS) and PNLMS++ Algorithms -- The Signed Regressor PNLMS Algorithm -- The Improved PNLMS (IPNLMS) Algorithms -- The Regular IPNLMS -- The IPNLMS with the L0 Norm -- The IPNLMS with a Norm-Like Diversity Measure --
505 0 _a6. The Exponentiated Gradient Algorithms -- Cost Function -- The EG Algorithm for Positive Weights -- The EG Algorithm for Positive and Negative Weights -- Link Between NLMS and EG Algorithms -- Link Between IPNLMS and EG Algorithms --
505 0 _a7. The Mu-Law PNLMS and Other PNLMS-Type Algorithms -- The Mu-Law PNLMS Algorithms -- The Sparseness-Controlled PNLMS Algorithms -- The PNLMS Algorithm with Individual Activation Factors --
505 0 _a8. Variable Step-Size PNLMS Algorithms -- Considerations on the Convergence of the NLMS Algorithm -- A Variable Step-Size PNLMS Algorithm --
505 0 _a9. Proportionate Affine Projection Algorithms -- Classical Derivation -- A Novel Derivation -- A Variable Step-Size Version --
505 0 _a10. Experimental Study -- Experimental Conditions -- IPNLMS Versus PNLMS -- MPNLMS, SC-PNLMS, and IAF-PNLMS -- VSS-IPNLMS -- PAPAs -- Bibliography -- Index -- Authors' Biographies.
506 1 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 3 _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.
530 _aAlso available in print.
588 _aTitle from PDF t.p. (viewed on June 4, 2010).
650 0 _aAdaptive filters
_xMathematical models.
650 0 _aEcho suppression (Telecommunication)
_xMathematical models.
653 _aNetwork and acoustic echo cancellation
653 _aAdaptive filters
653 _aSparseness
653 _aWiener
653 _aLMS
653 _aNLMS
653 _aVSS-NLMS
653 _aPNLMS
653 _aIPNLMS
653 _aEG
653 _aVSS-PNLMS
653 _aAPA
653 _aPAPA
700 1 _aBenesty, Jacob.
700 1 _aCiochină, Silviu.
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on speech and audio processing,
_x1932-1678 ;
_v# 6.
856 4 2 _3Abstract with links to resource
_uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6813553
999 _c237768
_d237768