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

By Yiteng Huang

ISBN-10: 0387276742

ISBN-13: 9780387276748

ISBN-10: 0387276769

ISBN-13: 9780387276762

ISBN-10: 3540376305

ISBN-13: 9783540376309

Telecommunication structures and human-machine interfaces have began utilizing a number of microphones and loudspeakers to render interplay extra life like, and extra effective. This increases acoustic sign processing difficulties less than multiple-input multiple-output (MIMO) eventualities, encompassing far-off speech acquisition, sound resource localization and monitoring, echo and noise keep watch over, resource separation and speech dereverberation, etc. The ebook opens with an acoustic MIMO paradigm, setting up basics, and linking acoustic MIMO sign processing with classical sign processing and communique theories. the second one a part of the publication provides a unique research of acoustic purposes performed within the paradigm to augment the basics of acoustic MIMO sign processing.

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

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Therefore, having a collection of acoustic channel impulse responses in diff'erent environments and/or under various climate conditions is essential to develop and evaluate effective acoustic signal processing algorithms. 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 /?

1) in the cross-correlation vector, we get. 42 3 Wiener Filter and Basic Adaptive Algorithms p = - E{s{k)x{k)} Rh. 54) where I is the identity matrix. 56) = Q^[h-h(fc)]. Thus, for the /th natural mode of the steepest-descent algorithm, we have [160]: vi{k) = (1 - fiXi)vi{k - 1), / = 1,2,. 58) The algorithm converges if, lim vi{k) = 0, V/. 59) k—*oo In this case. lim h(A;) = h. 5 Basic Adaptive Algorithms 43 where Amax is the largest eigenvalue of the correlation matrix R. Let us evaluate the time needed for each natural mode to converge to a given value.

Referring to the wall adjacent to the coordinate origin and subscript 1 the opposing wall. yj Reflection coefficients of the walls perpendicular to the y axis, with subscript 0 referring to the wall adjacent to the coordinate origin and subscript 1 the opposing wall. Pzo, Ai Reflection coefficients of the floor and ceiling, respectively. As seen from Fig. 26) where QX, Qy, and QZ are three integers. 26) as follows: r=[{-l)P^Xs + 2qxLx. 27) where Pa;,Py,^^ ^ {0,1}. While it is straightforward to calculate the distance that the wave travels from an image source (including the original) to the microphone, it is quite tricky to count how many reflections it experiences before reaching the microphone.

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

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