Download e-book for iPad: Adaptive Signal Processing: Applications to Real-World by Jacob Benesty, Yiteng Huang

By Jacob Benesty, Yiteng Huang

ISBN-10: 3642055079

ISBN-13: 9783642055072

ISBN-10: 3662110288

ISBN-13: 9783662110287

By adaptive sign processing, we suggest, as a rule, adaptive ?ltering.In- identified environments the place we have to version, determine, or music time-varying channels, adaptive ?ltering has been confirmed to be an e?ective and robust software. therefore, this software is now in use in lots of di?erent ?elds. because the invention, by way of Widrow and Ho? in 1959, of 1 of the ?rst advert- tive ?lters, the so-called least-mean-square, many purposes seemed to have the aptitude to exploit this primary thought. whereas the variety of - plications (using adaptive algorithms) has been (and retains) ?ourishing with time, because of a number of successes, the necessity for extra refined adaptive algorithms grew to become visible as real-world difficulties are extra complicated and extra tough. even supposing the speculation of adaptive ?ltering is already a well-established subject in sign processing, new and enhanced strategies are came across each year by means of researchers. a few of these fresh methods are mentioned during this ebook. The objective of this booklet is to supply, for the ?rst time, a connection with the most well liked real-world functions the place adaptive ?ltering concepts play a major function. to take action, we invited most sensible researchers in di?erent ?elds to c- tribute chapters addressing their speci?c subject of analysis. millions of pages wouldprobablynotbe enoughto describeallthe practicalapplicationsutil- ing adaptive algorithms. as a result, we constrained the themes to a couple vital purposes in acoustics, speech, instant, and networking, the place learn remains to be very energetic and open.

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Extra resources for Adaptive Signal Processing: Applications to Real-World Problems

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D. L. Duttweiler, "Proportionate normalized least me an square adaptation in eeho eaneelers," IEEE Trans . Speech Audio Processing, vol. 8, pp. 508-518, Sept. 2000. 6. S. 1. Hili and R . C. Williamson, "Convergenee of exponentiated gradient algorithms," IEEE Trans. Signal Processing, vol. 49, pp. 1208- 1215, June 2001. 22 J. Benesty et al. 7. D. R. Morgan and S. G. , vol. 3, pp. 245–247, Aug. 1996. 8. S. Amari, “Natural gradient works efficiently in learning,” Neural Computation, vol. 10, pp. 251–276, Feb.

In the scaled projection algorithm, the weights for adaptive beamforming are divided into a non-adaptive set of weights that gives unit gain in the desired look direction plus an adaptive set of weights that adjusts to the spatial characteristics of the incoming signal. A projection matrix is used to remove any component of the adaptive weights that would lie parallel to the non-adaptive weights, thus minimizing cumulative roundoff errors, and the 2-norm of the adaptive weight vector is clamped to reduce the sensitivity of the adaptive system to correlated interference and errors in the assumed sensor locations.

5. Block diagram of the run-time adaptive feedback-cancellation system. where dn−k (m) is the input to the adaptive filter, delayed by k samples, for block m. 13) where d(n) is the vector of present and past adaptive filter input samples. 3 Performance Metric A useful performance metric is the estimated maximum stable gain (MSG). 14) where H(ω) is the hearing aid gain, W (ω) is the feedback path model, and F (ω) is the actual feedback path response. 10) with z −1 = e−jω . The maximum stable hearing aid gain as a function of frequency is then |H(ω)| < 1 .

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Adaptive Signal Processing: Applications to Real-World Problems by Jacob Benesty, Yiteng Huang

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