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

By Yiteng Huang

Telecommunication platforms and human-machine interfaces have all started 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 away speech acquisition, sound resource localization and monitoring, echo and noise keep an eye on, resource separation and speech dereverberation, etc. The publication opens with an acoustic MIMO paradigm, constructing basics, and linking acoustic MIMO sign processing with classical sign processing and communique theories. the second one a part of the ebook provides a singular research of acoustic purposes performed within the paradigm to augment the basics of acoustic MIMO sign processing.

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

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100 H s 1 -; - H ;. i.. : •'::':! , . - | , ^ ^ ^ ^ ^ ^ ^ , . . „ ••• 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.

The reverberation energy in an acoustic impulse response decays exponentially over time, as shown in Fig. 2(a) by an example acoustic impulse response measured in the Bell Labs varechoic chamber. If the energy is measured in dB, then it decays linearly in time as clearly seen in Fig. 2(b). The reverberation time is determined from the estimated energy decay rate. By using Schroeder's backward integration method [270], a smoothed envelope for more accurate decay rate estimation can be obtained as illustrated in Fig.

44), XF R^^^ and x l R^'^^ are a good measure of the condition number of matrices R^'^ and R, respectively. Basically, there is no difference in the trend of the condition numbers of R and R^^^. In other words, if R^/^ is ill-conditioned (resp. well-conditioned) so is R. In the next subsection, we will show how to compute XF by using the Levinson-Durbin algorithm. 3 Fast Computation of the Condition Number In this subsection, we need to efficiently compute the two norms R / ^ J and R/^^.! • The calculation of the first one is straightforward.

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