LMS coupled adaptive prediction and system identification: a statistical model and transient mean analysis

Mboup, Mamadou; Bonnet, Madeleine; Bershad, N.J.

doi:10.1109/78.324727

Cited by 49 publications

(33 citation statements)

References 6 publications

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“…Hence, the recursion for is obtained by equating (23a) and (23b) (25) which, with the relation (26) completes the derivation of the URLS-FTF algorithm. Equation (25) defines the update relation of the vector in the URLS algorithm.…”

Section: Urls-ftf Algorithmmentioning

confidence: 82%

“…When the computation of the transversal filter is further approximated by using the LMS algorithm and the backward prediction error filter is discarded, a filtered-X LMS algorithm is obtained [26], [27]. In addition, theelement prediction error vector is approximated by a priori error samples of previous time instants except for the first element.…”

Section: A Approximation In Prediction Error Vectorsmentioning

confidence: 99%

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Underdetermined-order recursive least-squares adaptive filtering: the concept and algorithms

Baykal

Constantinides²

1997

IEEE Trans. Signal Process.

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Abstract-The concept of underdetermined recursive leastsquares (URLS) adaptive filtering is introduced. In particular, the URLS algorithm is derived and shown to be a direct consequence of the principle of minimal disturbance. By exploiting the Hankel structure of the filter input matrix, the fast transversal filter (FTF) version of the URLS algorithm (URLS-FTF) is derived including sliding window and growing window types, which allow alteration of the order of the URLS algorithm (which is equivalent to the linear prediction order of the input) in real time. The computational complexity is reduced to O(N) + O(m), where N is the adaptive filter length, and m is the order of the URLS algorithm. In addition, the efficient URLS (EURLS) algorithm, which does not compute the filter coefficients explicitly, thereby significantly reducing the computational load, is presented. Some earlier adaptive algorithms such as the averaged LMS, filtered-X LMS, and fast conjugate gradient are shown to be suboptimal approximations of the URLS algorithm. Instrumental variable approximations are also discussed. The URLS algorithm has a whitening effect on the input signal, which provides immunity to the eigenvalue spread of the input signal correlation matrix. Although the algorithm is sensitive to observation noise, it has good tracking characteristics, and tradeoffs can be found by tuning the step size. The utility of the URLS algorithms, in its basic form and FTF realization, depends heavily on the practical applicability of the mth-order sliding window estimate of the covariance matrix and mth-order FTF relations. The feasibility of the URLS family in practical applications is demonstrated in the simulations, which include channel equalization and acoustic echo cancellation in hands-free terminals with real data sets.

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Section: Urls-ftf Algorithmmentioning

confidence: 82%

Section: A Approximation In Prediction Error Vectorsmentioning

confidence: 99%

Underdetermined-order recursive least-squares adaptive filtering: the concept and algorithms

Baykal

Constantinides²

1997

IEEE Trans. Signal Process.

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“…This is due to the fact that the autocorrelation matrix of the input signal has a large eigenvalue spread. To overcome this problem by reducing the eigenvalue spread, whitening or decorrelated adaptive algorithms have been proposed for time-domain LMS [4][5][6]. In [4], the authors proposed a joint decorrelation of both the input and error signals.…”

Section: Introductionmentioning

confidence: 98%

A new pre-whitening transform domain LMS algorithm and its application to speech denoising

Chergui

Bouguezel

2017

Signal Processing

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“…To counteract the slow convergence of the LMS algorithm, several methods have been proposed [2] [3]. Most of them improve the convergence speed at the cost of large misadjustment and complexity.…”

Section: Introductionmentioning

confidence: 99%

High sampling rate audio signal modeling

Huang²,

Nallanathan³

Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.

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In this paper, we study the issue of the high sampling rate audio modeling for lossless audio coding. We propose a cascade LMS structure to successfully model all high sampling rate audio signals. This cascade structure predictor, not only performs better than its counterpart FIR linear prediction coding (LPC) technique in modeling general audio signals, but also displays a faster convergence and smaller mean square error (MSE) than conventional LMS predictor and low-order stages cascade LMS predictor, while the complexity of the proposed predictor remains simple. The simulation results show that the proposed structure gets better prediction gain compared with Monkey's audio codec and MPEG-4 ALS codec provided by Technology University of Berlin (TUB) for real high sampling rate audio test set. Other adaption algorithms can be used for the single stages.

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LMS coupled adaptive prediction and system identification: a statistical model and transient mean analysis

Cited by 49 publications

References 6 publications

Underdetermined-order recursive least-squares adaptive filtering: the concept and algorithms

Underdetermined-order recursive least-squares adaptive filtering: the concept and algorithms

A new pre-whitening transform domain LMS algorithm and its application to speech denoising

High sampling rate audio signal modeling

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