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 practical, 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 regulate, resource separation and speech dereverberation, and so on. The e-book 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 e-book offers a singular research of acoustic functions performed within the paradigm to enhance the basics of acoustic MIMO sign processing.

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5. Varechoic chamber at Bell Labs, (a) Floor plan (coordinate values measured in meters), (b) Photographic view of the chamber. different room characteristics can be simulated. The chamber provides great variability, flexibility, and speed of operation. In addition, the varechoic chamber was build with thick exterior concrete walls providing excellent isolation. There is an independent precision climate control system that allows control of room temperature to 1° Celsius and relative humidity to 1%.

Moreover, it can be shown using the CauchySchwarz inequahty (see [146] for example) that: llhlli < x/I||h||2. 25) As a result, ^(h) > 0. We see from these properties that the measure is bounded and is not affected by a non-zero scaling factor. Furthermore, the closer the measure is to 1, the sparser is the impulse response. On the contrary, the closer the measure is to 0, the denser or less sparse is the impulse response. Finally, since both the 1-norm and 2-norm of a vector are independent of the order of the vector coefficients, so is the defined sparseness measure.

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.

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