A performance bound for the LMS estimator

Quirk, Kevin J.; Milstein, L.B.; Zeidler, J.R.

doi:10.1109/18.841199

Cited by 18 publications

(16 citation statements)

References 12 publications

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“…We show some examples where the performance of the cascade LMS predictor performs better than the traditional LPC technique for synthetical and real audio signals. In the future, we devote to extend the work presented in the reference [4] to the performance bound of cascaded LMS predictor without "independence assumptions".…”

Section: Resultsmentioning

confidence: 99%

“…Some papers have reported cases where the performance of the LMS filter exceeds that of the finite Wiener filter [2,3]. Later, Quirk et al derived a performance bound for the LMS estimator without using "independence assumptions" and simply relying on the wide-sense stationarity of the signals, and showed some examples in which the LMS estimator outperforms the finite Wiener filter [4].…”

Section: Introductionmentioning

confidence: 99%

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A Performance Bound for a Cascade LMS Predictor

Huang

2005 IEEE International Symposium on Circuits and Systems

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The performance of an FIR cascade structure for adaptive linear prediction is studied, in which each stage FIR filter is independently adapted using LMS algorithm. In this paper, the performance bound is derived for cascade LMS predictor under some assumptions. We discover that this bound is possible to be better than that of the linear predictive coding (LPC) technique using block-based Levinson-Durbin algorithm if the cascade LMS predictor is well selected. We show some examples of this bound for synthetic and real audio signals in which a cascade LMS predictor outperforms the LPC technique using Levinson-Durbin algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Performance Bound for a Cascade LMS Predictor

Huang

2005 IEEE International Symposium on Circuits and Systems

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“…The affine projection order was selected to be P = 3, as the performance gains were minimal for higher orders, especially in the vocoder distorted channel (this is consistent with the findings of [56]). The in [59] for the case of adaptive equalisation, in [60] for adaptive linear prediction and in [61] for adaptive noise cancellation. In [62] near-end distortion was observed in a subband APA echo canceller, and was attributed to the colouration of the highly oversampled subband signals.…”

Section: P Erform Ance M Etricsmentioning

confidence: 99%

“…In [62] near-end distortion was observed in a subband APA echo canceller, and was attributed to the colouration of the highly oversampled subband signals. An in-depth review of the topic of non-Wiener effects in adaptive filtering can be found in [63], and summary of the explanation presented in [63] and [61] will be presented here.…”

Section: P Erform Ance M Etricsmentioning

confidence: 99%

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Acoustic echo cancellation for wideband VoIP

Laska¹

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Advances on the analysis of the LMS algorithm with a colored measurement noise

Lara

Haddad

Tarrataca

2019

SIViP

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Due to the inherent feedback feature of adaptive filtering algorithms, a comprehensive theoretical understanding of their learning process is still challenging. In order to make the mathematics tractable, most stochastic analyses adopt simplifications. One of these is the independence assumption, which presumes statistical independence between adaptive weights and input signal samples. Furthermore, the additive noise is usually assumed to be white, although it may not be the case in practice. This paper advances a novel theoretical model of the least-mean-square adaptive filter that, under the independence assumption, does not presume a white noise signal. Additionally, a theoretical result is established which implies that the stability properties of such an adaptive filter are not influenced by noise coloring. Such a result does not employ the above-mentioned assumptions, i.e., it is valid for both colored noise and non-infinitesimally small step size values. An optimal step-size sequence that minimizes the mean-square deviation and takes into account the stochastic coupling of the excitation data and adaptive weights is proposed. It is noteworthy that such a design does not induce divergence in practice and does not assume, for the first time, a white measurement noise. The theoretical contributions are confirmed by simulations.

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A performance bound for the LMS estimator

Cited by 18 publications

References 12 publications

A Performance Bound for a Cascade LMS Predictor

A Performance Bound for a Cascade LMS Predictor

Acoustic echo cancellation for wideband VoIP

Advances on the analysis of the LMS algorithm with a colored measurement noise

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