Download e-book for kindle: A Perspective on Stereophonic Acoustic Echo Cancellation by Jacob Benesty, Constantin Paleologu, Tomas Gänsler, Silviu

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. on the way to increase the conversation caliber of those platforms, an increasing number of stereophonic sound units with loudspeakers and microphones are deployed. end result of the coupling among loudspeakers and microphones, there is robust echoes, which make real-time conversation 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 process with genuine random variables. during this paintings, the authors recast this challenge right into a single-input/single-output process with complicated random variables due to the generally linear version. From this new handy formula, they re-derive crucial points of a SAEC, together with id of the echo paths with adaptive filters, double-talk detection, and suppression.

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R. Bitmead, B. Wahlberg, and A. Gustafsson, “LMS estimation via structural detection,” IEEE Trans. , vol. 46, pp. 2651–2663, Oct. 1998. 20. D. L. Duttweiler, “Proportionate normalized least-mean-squares adaptation in echo cancelers,” IEEE Trans. , vol. 8, pp. 508–518, Sept. 2000. 21. S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. , vol. 20, no. 1, pp. 33–61, 1998. 22. J. Benesty and S. L. Gay, “An improved PNLMS algorithm,” in Proc. IEEE ICASSP, 2002, pp.

9 VSS-IPNLMS Algorithm 45 where xl (n), l = 0, 1, . . , 2L − 1, are the components of the vector x(n). 103) is the normalized regularization parameter of the IPNLMS. It is interesting to observe that the regularization does not depend on the parameter κ. In fact, the regularization of the IPNLMS is equivalent to the regularization of the NLMS up to the scaling factor 2L, which is due to the definition of gl (n − 1). 6 to the IPNLMS. 95). The issue is to find an expression for the step-size parameter, μVSS (n), according to the constraint 2 E | (n)| =E = σv2 .

2L − 1, of the adaptive filter of the previous time, n − 1, are the best estimates available at time n, the ← − most obvious and reliable approximation for G(n) is ← − G(n) ≈ G(n − 1) = diag h0 (n − 1) , h1 (n − 1) , . . , h2L−1 (n − 1) . 86) in the context of adaptive filtering is G(n − 1)x(n)d∗ (n) ← − h (n) = H . 89) Second, we form a projection matrix for which h(n) spans its nullspace. We propose P(n) = I2L − G(n − 1)x(n)xH (n) . 90) It is easy to check that, indeed, P(n) is a (non-orthogonal) projection matrix since P2 (n) = P(n) [but PH (n) = P(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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