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 reasonable, 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 an eye on, resource separation and speech dereverberation, etc. The booklet opens with an acoustic MIMO paradigm, setting up basics, and linking acoustic MIMO sign processing with classical sign processing and verbal exchange theories. the second one a part of the ebook offers a unique research of acoustic purposes performed within the paradigm to enhance 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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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. 2(c). Since the noise floor limits the minimum level of the measured impulse response in Fig. 2(c), which leads to errors, we follow the general guideline suggested in [233] to truncate the measured impulse response at the knee where the main decay slope intersects the noise floor.

As will be clearly explained in the later relevant chapters, irreducible MIMO systems do not guarantee blind identifiability using only second-order statistics, but promise the existence of stable FIR MIMO equalizers according to the generalized Bezout identity [199]. 8 Sparse Impulse Response Recently, it has been recognized that most, if not all, acoustic channel impulse responses are sparse and this characteristics can be exploited by adaptive algorithms to improve their performance in terms of initial convergence and tracking.

2 summarizes this very important algorithm. 3 Sign Algorithms Up to now, the only cost function that we have used is the MSE. What makes this criterion so interesting is that an optimal solution (Wiener) can be easily derived as well as very powerful adaptive algorithms. An alternative to the MSE is the mean absolute error (MAE) [141], J a ( h ) = E{\e{k)\} = J5;{|x(/c)-h'^s(/c)|}. 2. The normalized LMS (NLMS) algorithm. 93) where /Xa is the adaptation step of the algorithm. This algorithm is simplified compared to the LMS since the L multiplications in the update equation are replaced by a sign change of the components of the signal vector s{k).

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


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