By Jacob Benesty, Yiteng Huang
By adaptive sign processing, we suggest, normally, adaptive ?ltering.In- identified environments the place we have to version, determine, or tune time-varying channels, adaptive ?ltering has been confirmed to be an e?ective and robust device. consequently, this software is now in use in lots of di?erent ?elds. because the invention, by means 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 proposal. whereas the variety of - plications (using adaptive algorithms) has been (and retains) ?ourishing with time, because of numerous successes, the necessity for extra subtle adaptive algorithms turned noticeable as real-world difficulties are extra complicated and extra tough. although the idea of adaptive ?ltering is already a well-established subject in sign processing, new and better suggestions are found each year by means of researchers. a few of these fresh ways are mentioned during this publication. The target of this booklet is to supply, for the ?rst time, a connection with the most popular real-world purposes the place adaptive ?ltering innovations play a tremendous function. to take action, we invited best researchers in di?erent ?elds to c- tribute chapters addressing their speci?c subject of research. hundreds of thousands of pages wouldprobablynotbe enoughto describeallthe practicalapplicationsutil- ing adaptive algorithms. for this reason, we restricted the subjects to a few very important functions in acoustics, speech, instant, and networking, the place examine remains to be very energetic and open.
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Extra resources for Adaptive Signal Processing: Applications to Real-World Problems
In the case of blind multichannel identiﬁcation, a properly aligned vector is still a valid solution to the impulse response ht even though their gains may diﬀer. 5 5 Fig. 1. Sparse impulse response used in simulations. 5 4 Fig. 2. Misalignment of the NLMS (++), NEGU± (xx), and NEG± (–) algorithms with white Gaussian noise as input signal and u = 400 ht 1 for the NEG± algorithm. ﬁrst experiment is used here. 75) J. Benesty et al. 5 Fig. 3. Misalignment of the NLMS (++), IPNLMS (- - ), and NEG± H algorithms with white Gaussian noise as input signal and u = 411 ht l11 for the NEG± algorithm.
4 are the same as in Fig. 3. According to this simulation, the IPNLMS and NEG± algorithms track much better than the NLMS algorithm. In Fig. 5, the FRLS algorithm is compared to the FERLS algorithm with u = 15 ht 1 : while the initial convergence of the two algorithms is almost the same, the FERLS tracks faster than the FRLS. Finally, Fig. 6 shows the misalignment of the NLMS, IPNLMS, and the NEG± algorithms with a speech source as input signal and u = 4 ht 1 for the NEG± algorithm. Again, the IPNLMS and NEG± algorithms behave better than NLMS with a small advantage for the IPNLMS over the NEG± due to the fact that it is better normalized with a non-stationary input signal.
The impulse response is a sequence of 255 samples, but most of its energy is contained in the ﬁrst 32 samples following the overall delay. Thus the tail of the impulse response consists almost entirely of room noise and reverberation, and the RMS room noise is computed from the last 64 samples in the impulse response. The peak-to-RMS room noise ratio thus gives an indication of the quality of the estimated feedback path impulse response. A peak-to-noise ratio of 30 dB or better has been empirically found to yield good estimates of the response poles and zeros.