Download Acoustic MIMO Signal Processing (2006) (Signals and by Yiteng Huang PDF

By Yiteng Huang

Telecommunication structures and human-machine interfaces have began utilizing a number of microphones and loudspeakers to render interplay extra sensible, and extra effective. This increases acoustic sign processing difficulties below multiple-input multiple-output (MIMO) situations, encompassing far-off speech acquisition, sound resource localization and monitoring, echo and noise regulate, resource separation and speech dereverberation, and so on. The booklet opens with an acoustic MIMO paradigm, developing basics, and linking acoustic MIMO sign processing with classical sign processing and conversation theories. the second one a part of the publication provides a singular research of acoustic purposes performed within the paradigm to enhance the basics of acoustic MIMO sign processing.

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Extra info for Acoustic MIMO Signal Processing (2006) (Signals and Communication Technology)

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1 - **% I 100 200 300 400 Time i (ms) (c) •• 1 ••'••'•• .... J ^ 500 600 Fig. 2. Illustration of the backward integration method for reverberation time estimation, (a) Sample impulse response measured in the varechoic chamber at Bell Labs, (b) Squared impulse response, (c) Backward integration of squared impulse response with truncation time 400 ms (solid) and Hnear fitting curve (dashed). that perfect deconvolution of an acoustic channel can be accomplished only with an acausal filter.

Using the stability condition, \€{k)\ < \e{k)\, we deduce that: s^{k)s{k) Another way to simplify the LMS filter is to replace s{k) with its sign. 98) The algorithms derived in this subsection are very simple to implement and can be very useful in some applications. However, their convergence rate is usually lower than the LMS and their excess MSE is higher [17], [81], [50]. 6 IVEIMO Wiener Filter In this section, we consider a MIMO system with M inputs and A'' outputs (see Chap. 102) where hn: is an estimate of h^;.

One easy way to derive the stochgistic gradient algorithm is by approximating the deterministic algorithm. Indeed, in practice, the two quantities and R = E{s{k)s^{k)} are in general not known. 75) and replace them in the steepest-descent algorithm [eq. 49)], we get: h(/c) - h(/c - 1) -f /i [p(/c) - R(/c)h(/c - 1)1 - h(/c - 1) + /is(/c) \x{k) - s^{k)h{k - 1)1 . 5 Basic Adaptive Algorithms 45 This simple recursion is the LMS algorithm. Contrary to the deterministic algorithm, the LMS weight vector h(/c) is now a random vector.

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