By Jacob Benesty, Constantin Paleologu, Tomas Gänsler, Silviu Ciochină
Single-channel hands-free teleconferencing structures have gotten renowned. with the intention to improve the verbal exchange caliber of those structures, an increasing number of stereophonic sound units with loudspeakers and microphones are deployed. due to the coupling among loudspeakers and microphones, there's powerful echoes, which make real-time conversation very tough. the way in which we all know to cancel those echoes is through a stereo acoustic echo canceller (SAEC), that are modelled as a two-input/two-output process with actual random variables. during this paintings, the authors recast this challenge right into a single-input/single-output procedure with advanced random variables because of the commonly 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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Additional resources for A Perspective on Stereophonic Acoustic Echo Cancellation
Widrow, “Adaptive ﬁlters,” in Aspects of Network and System Theory, R. E. Kalman and N. , New York: Holt, Rinehart and Winston, 1970. 7. J. Benesty, T. G¨ ansler, D. R. Morgan, M. M. Sondhi, and S. L. Gay, Advances in Network and Acoustic Echo Cancellation. Berlin, Germany: Springer-Verlag, 2001. 8. B. C. J. Moore, An Introduction to the Psychology of Hearing. London, UK: Academic Press, 1989. 9. D. R. Morgan, J. L. Hall, and J. Benesty, “Investigation of several types of nonlinearities for use in stereo acoustic echo cancellation,” IEEE Trans.
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 . , 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, . . , P − 1. Our goal is to ﬁnd an expression for the normalized step-size parameter αp (n) such that E | p (n)| 2 = E |v(n − p)| 2 .
11. M. S. Asif and J. Romberg, “Dynamic updating for 1 minimization,” IEEE J. Sel. , vol. 4, pp. 421–434, Apr. 2010. Chapter 4 A Class of Stochastic Adaptive Filters In this chapter, we derive, study, and analyze a class of stochastic adaptive ﬁlters for SAEC with the WL model. All developed algorithms try to converge to the optimal Wiener ﬁlter. We start with the classical stochastic gradient algorithm, which is a good approximation of the deterministic algorithm studied in the previous chapter.
A Perspective on Stereophonic Acoustic Echo Cancellation by Jacob Benesty, Constantin Paleologu, Tomas Gänsler, Silviu Ciochină
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