By Jacob Benesty, Yiteng Huang
By adaptive sign processing, we suggest, usually, adaptive ?ltering.In- recognized 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 device. consequently, 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 primary inspiration. whereas the variety of - plications (using adaptive algorithms) has been (and retains) ?ourishing with time, because of numerous successes, the necessity for extra refined adaptive algorithms grew to become noticeable as real-world difficulties are extra complicated and extra not easy. even supposing the idea of adaptive ?ltering is already a well-established subject in sign processing, new and more desirable techniques are came across each year through researchers. a few of these contemporary methods are mentioned during this e-book. The aim of this ebook is to supply, for the ?rst time, a connection with the most popular real-world functions the place adaptive ?ltering options play a huge function. to take action, we invited best 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. accordingly, we restricted the subjects to a few vital purposes in acoustics, speech, instant, and networking, the place study continues to be very lively and open.
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Additional info for Adaptive Signal Processing: Applications to Real-World Problems
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. 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.
2. Feedback path magnitude frequency response for a telephone handset removed (dashed line) and placed near the ear (solid line) for a behind-the-ear (BTE) hearing aid connected to an unvented earmold on a dummy head. 5 to 4 kHz. These diﬀerences between the two earmolds illustrate the variety of possible feedback paths and the need to estimate the feedback path model parameters for each individual ﬁtting. Bringing the telephone handset up to the ear results in an increase of about 10 dB in the amplitude of the transfer functions independent of the type of earmold used.
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. 6; all the others were done with a white Gaussian input signal. 2 compares the NLMS, NEGU±, and NEG± algorithms when a large u (here u = 400 ht 1 ) is chosen for the NEG± algorithm.
Adaptive Signal Processing: Applications to Real-World Problems by Jacob Benesty, Yiteng Huang