Characterization of a Cascade LMS Predictor

Huang, Dawei; Su, X.-R.; Nallanathan, Arumugam

doi:10.1109/icassp.2005.1415674

Cited by 5 publications

(8 citation statements)

References 3 publications

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“…Consequently, the eigenvalue spread becomes smaller in each stage, which results in the faster convergence [11]. Basically, the simulation results confirm the analysis in theory.…”

Section: Study Of Cascade Structure For Signal Modelingsupporting

confidence: 87%

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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“…Consequently, the eigenvalue spread becomes smaller in each stage, which results in the faster convergence [11]. Basically, the simulation results confirm the analysis in theory.…”

Section: Study Of Cascade Structure For Signal Modelingsupporting

confidence: 87%

High sampling rate audio signal modeling

Huang²,

Nallanathan³

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

Self Cite

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“…Note that the structure proposed here is different compared to the cascade structures in [7] and [8]. In [7] the LMS filters were applied for linear prediction and thus not in the context of system identification. In [8] the purpose of the filters is system identification but the update of both is performed by the same error leading to poor quality.…”

Section: Cascaded Adaptive Fir Filtersmentioning

confidence: 99%

“…While in [7] the purpose was linear prediction, in [8] system identification in the context of echo cancellation was the main focus. We will show that cascaded structures can be approached by the proposed coupled filter algorithm and thus can be treated by our theory.…”

Section: Introductionmentioning

confidence: 99%

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On robustness of coupled adaptive filters

Dallinger¹,

Rupp²

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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We provide a time domain analysis of the robustness and stability performance for coupled adaptive algorithms of gradient type. The considered coupling may occur inherently as well as by desire of the designer. The presented analyses focus on system identification. Examples are presented to investigate convergence and steady-state behaviour by simulations which are compared to theory. In particular, the presented approach allows for a deeper understanding of cascaded adaptive filters in terms of robustness and l 2 -stability.

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“…The motivation can be as simple as dividing a long filter in shorter autonomous parts or owing to structural purposes [59,60]. In the context of the identification of non-linear power amplifiers of large bandwidth, a concatenation of linear filter parts with memory and nonlinear parts without memory is very common.…”

Section: Cascaded Adaptive Algorithmsmentioning

confidence: 99%

Adaptive filters: stable but divergent

Rupp

2015

EURASIP J. Adv. Signal Process.

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The pros and cons of a quadratic error measure in the context of various applications have often been discussed. In this tutorial, we argue that it is not only a suboptimal but definitely the wrong choice when describing the stability behavior of adaptive filters. We take a walk through the past and recent history of adaptive filters and present 14 canonical forms of adaptive algorithms and even more variants thereof contrasting their mean-square with their l 2 −stability conditions. In particular, in safety critical applications, the convergence in the mean-square sense turns out to provide wrong results, often not leading to stability at all. Only the robustness concept with its l 2 −stability conditions ensures the absence of divergence.

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Characterization of a Cascade LMS Predictor

Cited by 5 publications

References 3 publications

High sampling rate audio signal modeling

High sampling rate audio signal modeling

On robustness of coupled adaptive filters

Adaptive filters: stable but divergent

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