By Jacob Benesty, Yiteng Huang

ISBN-10: 3642055079

ISBN-13: 9783642055072

ISBN-10: 3662110288

ISBN-13: 9783662110287

By adaptive sign processing, we suggest, in most cases, adaptive ?ltering.In- identified environments the place we have to version, determine, or song time-varying channels, adaptive ?ltering has been confirmed to be an e?ective and robust software. hence, this device 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 looked as if it would have the capability to take advantage of this basic inspiration. 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 subtle adaptive algorithms grew to become noticeable as real-world difficulties are extra complicated and extra hard. although the speculation of adaptive ?ltering is already a well-established subject in sign processing, new and better techniques are came across each year via researchers. a few of these fresh methods are mentioned during this booklet. The objective of this publication is to supply, for the ?rst time, a connection with the most well liked real-world purposes the place adaptive ?ltering thoughts play an incredible position. to take action, we invited most sensible 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. hence, we constrained the subjects to a couple very important functions in acoustics, speech, instant, and networking, the place examine continues to be very energetic and open.

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The same length is used for all the adaptive ﬁlters 16 J. Benesty et al. h(n). The sampling rate is 8 kHz and a white noise signal with 30 dB SNR (signal-to-noise ratio) is added to the output y(n). The input signal x(n) is either white Gaussian noise or a speech signal. 001, λ = 1 − 1/(3L), δ = 20σx2 , δIPNLMS = (1 − κ)δ/2L. 6 show the convergence of the normalized misalignment, ht − h(n) 2 / ht 2 , for all the algorithms. The only simulation that was done with an input speech signal is shown in Fig.

In the ﬁrst approach, the distance of the adaptive ﬁlter coeﬃcients from the reference coeﬃcients is determined, and the norm of the vector formed by the set of adaptive ﬁlter coeﬃcients is clamped to prevent the distance from exceeding a pre-set threshold. In the second approach, a cost function is used in the adaptation to penalize excessive deviation of the adaptive ﬁlter coeﬃcients from the reference coeﬃcients. 3) is satisﬁed. The transfer function of the actual feedback path is not available, so the feedback path is approximated instead by the reference set of adaptive ﬁlter coeﬃcients determined during initialization.

Amari, “Natural gradient works eﬃciently in learning,” Neural Computation, vol. 10, pp. 251–276, Feb. 1998. 9. J. Benesty and S. L. Gay, “An improved PNLMS algorithm,” in Proc. IEEE ICASSP, 2002. 10. R. K. Martin, W. A. Sethares, R. C. Williamson, and C. 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.

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