By Jacob Benesty, Tomas Gänsler, Dennis R. Morgan, M. Mohan Sondhi, Steven L. Gay (auth.)

ISBN-10: 364207507X

ISBN-13: 9783642075070

ISBN-10: 3662044374

ISBN-13: 9783662044377

This publication brings jointly many complicated subject matters in community and acoustic echo cancellation that are aimed in the direction of bettering the echo cancellation functionality of next-generation telecommunication platforms. the overall topic nature pertains to algorithms with elevated convergence pace, better detection of double-talk from near-end speech, powerful immunity to undetected double-talk, elevated computational potency, and multi-channel potential. The ensuing compendium offers a coherent therapy of such subject matters now not came upon another way in journals or different books. The chapters are comparable with a typical terminology, yet nonetheless could be learn independently.

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2. 6) (sign [x(n)]e(n) = sign [x(n)]{[h - h(n)f x(n)+w(n)}) is proportional to the magnitude of x(n) for w(n) smaIl. 7). 1). 3). Our motivation for doing this is to remedy the reduced convergence rate that results from using the SR-LMS, compared to NLMS, and achieve a reduction of the complexity compared to PNLMS. t G( n)sign [x(n)]e(n) + xT(n)G(n)sign [x(n)] + o/{xT(n)sign [x(n)] + or} . 7). 8) still has a rather high numerical complexity because of the term, xT(n)G(n)sign [x(n)], which consumes L multiplications.

23) S where S is the scale factor and Al is a forgetting factor. , s scales the value of J s . We have chosen xO as, 44 2. 18) and ß is a positive constant. 1, is purely for reducing computational complexity. Other bounded even functions would also suffice since the choice of X(·) turns out to be not very critical. For normalization, ß is chosen such that for a zero-mean, unit-variance Gaussian process z, E{X(z)} = O. 26) e- t 2 dt. Upon convergence, the scale estimate s = (Tv for Gaussian noise, where (Tv is the desired standard deviation of the (near-end) ambient noise.

For normalization, ß is chosen such that for a zero-mean, unit-variance Gaussian process z, E{X(z)} = O. 26) e- t 2 dt. Upon convergence, the scale estimate s = (Tv for Gaussian noise, where (Tv is the desired standard deviation of the (near-end) ambient noise. A recursive scale estimator can then be derived with a Newton technique. Let sen + 1) = sen) - [\7 s J s t 1 J s . 27) is found by introducing the foIlowing approximations, Js(n) U(n) = X = [I:~~~I] , E{\7 s J s (n)} . 31 ) where (2 lk 2 1')=y;(1-e- 20 ) .

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Advances in Network and Acoustic Echo Cancellation by Jacob Benesty, Tomas Gänsler, Dennis R. Morgan, M. Mohan Sondhi, Steven L. Gay (auth.)


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