By Dr. Yiteng Arden Huang, Dr. Jingdong Chen, Prof. Dr. Jacob Benesty (auth.)
Acoustic MIMO sign Processing
Yiteng (Arden) Huang
Telecommunication structures and human-machine interfaces begin using a number of microphones and loudspeakers with the intention to make conversations and interactions extra realistic, therefore extra effective. This improvement provides upward push to a number of acoustic sign processing difficulties below 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 forth. the decade has witnessed a becoming curiosity in exploring those difficulties, yet there was little attempt to advance a conception to have these kind of difficulties investigated in a unified framework. This special publication makes an attempt to fill the gap.
Acoustic MIMO sign Processing is split into significant components - the theoretical and the sensible. The authors start by way of introducing an acoustic MIMO paradigm, constructing the elemental of the sphere, and linking acoustic MIMO sign processing with the techniques of classical sign processing and conversation theories when it comes to approach id, equalization, and adaptive algorithms. within the moment a part of the e-book, a unique and penetrating research of aforementioned acoustic functions is performed within the paradigm to augment the elemental recommendations of acoustic MIMO sign processing.
Acoustic MIMO sign Processing is a well timed and demanding expert reference for researchers and working towards engineers from universities and a variety of industries. it's also an exceptional textual content for graduate scholars who're attracted to this fascinating field.
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Additional info for Acoustic MIMO Signal Processing
7(a), the path from the image to the microphone crosses in the x direction the same number (l^xl) of evenindexed and odd-indexed surfaces. Then the reduction in wave amplitude due to the reflections is P. 28) For Pa; = 1 as seen from Fig. 7(b), the number of even-indexed surfaces crossed is different from the number of odd-indexed surfaces crossed by 1. 29) yields the reduction in wave amplitude due to reflections from surfaces perpendicular to the x axis, given by /3. = 0li^-''^^f3l\^K Px = 0 , 1 .
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.
01 of the sampling frequency is imposed, as suggested in . 2. 4 Summary In this chapter, we described four signal models for acoustic systems according to the number of inputs and outputs. The models were presented in both the time and frequency domains and will be frequently used throughout the rest of this book. ) but also those characteristics that have not received adequate attention until very recently (like multichannel diversity, the common-zero problem, and sparseness). At the end of this chapter, we discussed facilities 30 2 Acoustic MIMO Systems for directly measuring acoustic impulse responses and the image method for their simulation.
Acoustic MIMO Signal Processing by Dr. Yiteng Arden Huang, Dr. Jingdong Chen, Prof. Dr. Jacob Benesty (auth.)