Jacob Benesty, Jingdong Chen, Yiteng Huang (auth.)'s Microphone Array Signal Processing PDF

By Jacob Benesty, Jingdong Chen, Yiteng Huang (auth.)

ISBN-10: 3540786112

ISBN-13: 9783540786115

ISBN-10: 3540786120

ISBN-13: 9783540786122

Microphone arrays have attracted loads of curiosity within the final 20 years. the explanation at the back of this can be that they've the capability to unravel many very important difficulties in either human-machine and human-human interfaces for other kinds of communications. yet sooner than microphone arrays could be deployed commonly, there's a powerful want for a deep knowing of the issues encountered within the genuine global and their transparent formula so that necessary algorithms may be constructed to strategy the sensor signals.

While there are lots of manuscripts on antenna arrays from a narrowband viewpoint (narrowband signs and narrowband processing), the literature is sort of scarce in terms of sensor arrays defined from a really broadband point of view. Many algorithms for speech purposes have been easily borrowed from narrowband antenna arrays. even if, a right away software of narrowband principles to broadband speech processing will not be unavoidably applicable and will bring about many misunderstandings. hence, the most goal of this publication is to derive and clarify the main primary algorithms from a strictly broadband (signals and/or processing) standpoint. due to the method taken right here, new options are available gentle that experience the good power of fixing a number of, very tricky difficulties encountered in acoustic and speech applications.

Microphone Array sign Processing is a well timed and significant expert reference for researchers and practising engineers from universities and quite a lot of industries. it's also a very good textual content for graduate scholars who're drawn to this promising and interesting field

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Extra info for Microphone Array Signal Processing

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108) σx2 hT Rxx h and ρ2 hT x, hT y = The SPCC ρ2 x, hT x can be viewed as a speech-distortion index. If h = h1 (no speech distortion) then ρ2 x, hT x 2 ρ = 1. The closer the value of T x, h x is to 0, the more distorted the speech signal (except for a sim- ple delay filter). The SPCC ρ2 hT x, hT y shows the SNR improvement, so it can be viewed as a noise-reduction index that reaches its maximum when SNR(h) is maximized. Property 1 is fundamental in the noise-reduction problem. It shows that the SPCC ρ2 x, hT y , which is a cost function as explained later, is simply the product of two important indices reflecting noise reduction and speech distortion.

This means that if we want to adjust the beam pattern, we have to make physical changes to the array geometry, which is virtually impossible once an array system is delivered. A legitimate question then arises: can we improve the array performance with some signal processing techniques to adjust its beam pattern without changing its geometry? We attempt to answer this question in this section and discuss a class of techniques called fixed beamforming, which takes into account the array geometry but assumes no information from neither the source nor the noise signals.

Particular Case 2: Here, we only assume that the noise signals have the same energy and that all the attenuation factors are equal to 1. 9) where ρs = ρvi vj = 2 N N −1 N ρvi vj , i=1 j=i+1 vi vj σ vi σ vj . ρvi vj is the correlation coefficient with |ρvi vj | ≤ 1. Normally, this coefficient ranges between −1 and 1. , ρvi vj = 1, we have i=1 j=i+1 ρvi vj = N (N − 1)/2. In this case, oSNR = SNR. So, no gain is possible with the DS technique. As the value of the correlation coefficient ρvi vj decreases from 1 to 0, the gain in SNR increases.

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Microphone Array Signal Processing by Jacob Benesty, Jingdong Chen, Yiteng Huang (auth.)


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