By Jacob Benesty, Yiteng Huang
By adaptive sign processing, we suggest, commonly, adaptive ?ltering.In- recognized environments the place we have to version, determine, or song time-varying channels, adaptive ?ltering has been confirmed to be an e?ective and strong software. hence, this device is now in use in lots of di?erent ?elds. because the invention, through Widrow and Ho? in 1959, of 1 of the ?rst advert- tive ?lters, the so-called least-mean-square, many functions seemed to have the capability to exploit this basic thought. whereas the variety of - plications (using adaptive algorithms) has been (and retains) ?ourishing with time, due to a number of successes, the necessity for extra refined adaptive algorithms grew to become visible as real-world difficulties are extra complicated and extra hard. even supposing the idea of adaptive ?ltering is already a well-established subject in sign processing, new and stronger recommendations are found each year by way of researchers. a few of these contemporary techniques are mentioned during this e-book. The aim of this e-book is to supply, for the ?rst time, a connection with the most well liked real-world purposes the place adaptive ?ltering innovations play a massive function. to take action, we invited most sensible researchers in di?erent ?elds to c- tribute chapters addressing their speci?c subject of analysis. hundreds of thousands of pages wouldprobablynotbe enoughto describeallthe practicalapplicationsutil- ing adaptive algorithms. for this reason, we constrained the themes to a few vital purposes in acoustics, speech, instant, and networking, the place learn continues to be very lively and open.
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Additional info for Adaptive Signal Processing: Applications to Real-World Problems
The characteristics of the feedback path being modeled are then described. An additional concern in designing signal processing for a hearing aid are the processing constraints imposed by a low-power portable device, and these concerns are discussed next. Two forms of constrained adaptation are then derived, and simulation results are used to give a comparison of the constrained adaptation with the conventional unconstrained approach for a system adapting without a probe signal. Further improvements in feedback cancellation performance, particularly a reduction in audible processing artifacts, can be achieved by using the ﬁltered-X algorithm, and this approach is described next.
Misalignment, during impulse response change, of the FRLS (- ) and FERLS (--) algorithms with white Gaussian noise as input signal and u = 1511 htlll für the FERLS algorithm. 0 NLMS iii' ll. ~ ! 5 Fig. 6. Misalignment of the NLMS (+ + ), IPNLMS (- - ), and NEG ± (- ) algorithms with a speech source as input signal and u = 411 htlll for the NEG± algorithm. is the projection misalignment vector. By projecting h t onto h(n) and defining a projection error, we take into account only the intrinsic misalignment of the channel estimate, disregarding an arbitrary gain factor .
R. , “Exploiting sparsity in adaptive ﬁlters,” in Conference on Information Sciences and Systems, The John Hopkins University, 2001. 11. S. L. Gay and S. C. Douglas, “Normalized natural gradient adaptive ﬁltering for sparse and nonsparse systems,” in Proc. IEEE ICASSP, 2002. 12. Y. Huang and J. Benesty, “Adaptive multi-channel least mean square and Newton algorithms for blind channel identiﬁcation,” Signal Processing, vol. 82, pp. 1127–1138, Aug. 2002. 13. D. R. Morgan, J. Benesty, and M. M. , vol.