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

By Yiteng Huang

Telecommunication platforms and human-machine interfaces have started utilizing a number of microphones and loudspeakers to render interplay extra sensible, and extra effective. This increases acoustic sign processing difficulties less than multiple-input multiple-output (MIMO) situations, encompassing far away speech acquisition, sound resource localization and monitoring, echo and noise keep watch over, resource separation and speech dereverberation, and so on. The ebook 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 booklet offers a unique research of acoustic functions performed within the paradigm to enhance the basics of acoustic MIMO sign processing.

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In this section, we will describe the facilities that were built to directly measure acoustic channel impulse responses and methods that were developed for their simulation. 22 2 Acoustic MIMO Systems 20 2S 30 Decay Constant^ (b) Fig. 4. Sparseness measures of exponentially decaying filters with various decay constants, (a) Impulse responses he of length L — 256 for values of the decay constant /? from 1 to 50. (b) Sparseness measure for ho as a function of the decay constant, 0. 1 Direct M e a s u r e m e n t of A c o u s t i c I m p u l s e R e s p o n s e s In order to measure acoustic impulse responses under various conditions, we need a room whose acoustics can be controlled.

Soon after he published his work, Levinson formulated the same problem in discrete time [214]. Levinson's contribution has had a great impact on the field. Indeed, thanks to him, Wiener's ideas have become more accessible to many engineers. A very nice overview of linear filtering theory and the history of the different discoveries in this area can be found in [198]. In this chapter, we will show that the Wiener theory plays a fundamental role in room acoustics. For example, in many acoustic applications, the impulse response between a loudspeaker and a microphone needs to be identified.

10) Rfhf,o = Pf, where Rf = £;{sf(fc)sf(fc)} is the correlation matrix of the signal s{k) and Pt = E {s{{k)xik)} is the cross-correlation vector between Sf(A;) and x{k). The correlation matrix is symmetric and positive semidefinite. e. a matrix which has constant values along diagonals, r(0) r(l) r(l) r(0) r(Lf - 1) r{L{ - 2) r(Lf-2) r(0) Rf = Lr(Lf-l) with r{l) = E{s{k)s{k - I)}, / = 0,1,• • • ,Lf - 1. In the SISO system case, this matrix is usually positive definite even for quasi-stationary signals like speech; however, it can be very ill conditioned.

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