A Perspective on Stereophonic Acoustic Echo Cancellation by Jacob Benesty, Constantin Paleologu, Tomas Gänsler, Silviu PDF

By Jacob Benesty, Constantin Paleologu, Tomas Gänsler, Silviu Ciochină

ISBN-10: 364222573X

ISBN-13: 9783642225734

ISBN-10: 3642225748

ISBN-13: 9783642225741

Single-channel hands-free teleconferencing structures have gotten renowned. with a view to increase the communique caliber of those structures, progressively more stereophonic sound units with loudspeakers and microphones are deployed. as a result of coupling among loudspeakers and microphones, there is robust echoes, which make real-time verbal exchange very tricky. the way we all know to cancel those echoes is through a stereo acoustic echo canceller (SAEC), which are modelled as a two-input/two-output approach with actual random variables. during this paintings, the authors recast this challenge right into a single-input/single-output approach with complicated random variables due to the generally linear version. From this new handy formula, they re-derive an important features of a SAEC, together with identity of the echo paths with adaptive filters, double-talk detection, and suppression.

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Extra resources for A Perspective on Stereophonic Acoustic Echo Cancellation

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L. Gay, “An improved PNLMS algorithm,” in Proc. IEEE ICASSP, 2002, pp. 1881–1884. 23. C. Paleologu, J. Benesty, and S. Ciochin˘ a, “A variable step-size proportionate NLMS algorithm for echo cancellation,” Revue Roumaine des Sciences Techniques – Serie Electrotechnique et Energetique, vol. 53, no. 3, pp. 309–317, 2008. 24. J. Benesty, F. Amand, A. Gilloire, and Y. Grenier, “Adaptive filtering algorithms for stereophonic acoustic echo cancellation,” in Proc. IEEE ICASSP, 1995, pp. 3099–3102. Chapter 5 A Class of Affine Projection Algorithms Affine projection algorithms (APAs) are very good candidates for echo cancellation.

41) is obtained when α0 (n) = α1 (n) = · · · = αP −1 (n) = α. 2), the posteriori error vector becomes (n) = [IP − Dα (n)] e(n). , (n) = 0. 44) that Dα (n) = IP . 41), with the step size α = 1. , v(n) = 0, we deal with an ideal “system identification” configuration. In this case, the value of the step size α = 1 makes sense, because it leads to the best performance [1]. , to recover the system noise from the error of the adaptive filter. 45) where p (n) and ep (n) denote the (p + 1)th elements of the vectors (n) and e(n), with p = 0, 1, .

Both vectors are obviously in the nullspace of P(n). 54) which is not affected by h (n − 1). 51) is that ← − h (n) is, in fact, the minimum 2 -norm solution of the linear system of one ← −H equation d(n) = h (n)x(n). 45). And, of course, h(n) is also a solution of d(n) = h (n)x(n). Therefore, h (n − 1) can be seen as a good initialization of the adaptive filter since the minimum 2 -norm solution is not the optimal solution. The interpretation [5] of the NLMS algorithm is the following. First, we ← −H find the minimum 2 -norm solution of d(n) = h (n)x(n), which is denoted ← − by h (n).

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A Perspective on Stereophonic Acoustic Echo Cancellation by Jacob Benesty, Constantin Paleologu, Tomas Gänsler, Silviu Ciochină

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