A Fast Converging Algorithm for Network Echo Cancelation

Nekuii, M.; Atarodi, M.

doi:10.1109/lsp.2004.824027

Cited by 28 publications

(14 citation statements)

References 3 publications

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“…In this section, we evaluate the ability of combination schemes to improve the performance of IPNLMS using echo cancellation scenarios similar to those encountered in, e.g., [3,4,15,8]. Different echo paths will be used, all with length Al == 512 and an attenuation of 10 dB.…”

Section: Methodsmentioning

confidence: 99%

Adaptive combination of IPNLMS filters for robust sparse echo cancellation

Arenas‐García

Figueiras-Vidal

2008

2008 IEEE Workshop on Machine Learning for Signal Processing

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Fig. 1. Block diagram for an adaptive echo cancellation configuration.[4], we define the degree of sparsity of a chanlle] as a qualitative measure ranging from strongly dispersive (when most of the coefficients of W o (n) are active) to strongly sparse.It is a well-known fact that adaptive schemes which distribute the adaptation energy equally among all filter coefficients~such as least-mean-square (LMS) and normalized LMS (NLI\1S), exhibit a very slow convergence for filters with many taps [5, 6], making the application of such schemes unpractical for the cancellation of sparse echo channels. To alleviate this problem, the proportionate NLMS algorithm (PNLMS)[7] makes the adaptation step for each tap proportional to the current absolute value of the estimated weight, i.e, s(n) + eo(n) d(n)

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Section: Methodsmentioning

confidence: 99%

Adaptive combination of IPNLMS filters for robust sparse echo cancellation

Arenas‐García

Figueiras-Vidal

2008

2008 IEEE Workshop on Machine Learning for Signal Processing

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“…The factors that influence the choice of the adaptation algorithm are the speed of convergence to optimal operating condition, the minimum error at convergence and computational complexity. Lots of research works are focused on quickening the convergence speed and reducing the minimum error [8], [9], and there is a few to pay attentions to decreasing the computational complexity.…”

Section: Adaptive Filtermentioning

confidence: 99%

Multirate Algorithm for Updating the Coefficients of Adaptive Filter

Dong

2008

2008 First International Conference on Intelligent Networks and Intelligent Systems

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This paper presents a novel algorithm which can dynamically change the update rate of the coefficients of adaptive filter by analysing the actual application environments. This algorithm builds a nonlinear function relationship between the update rate and the minimum error which can automatically adjust the update rate. When the environment is varying, the rate is increased, while it would be decreased when the environment is stable. And the computation complexity of adaptive filter can be significantly reduced. This is because most multiplications that cost in adaptive filter is consumed by calculating the coefficients. Acoustic echo cancellation (AEC) experiments indicate that the scheme proposed in this paper performs significantly better than traditional algorithms.

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“…As a result, a concept for developing sparse adaptive filters to handle such problem is becoming a hot topic for all researchers [4][5][6][7][8][9][10][11][12]. For sparse systems, most of their coefficients take the values of zero or near-zeros, while only a few coefficients have significant values [13][14][15][16]. Such sparse systems are commonly encountered in our in-nature world and plenty of real-world engineering applications such as the multi-path channels in wireless communications [17,18], underwater communications [19,20] and acoustic channels [21].…”

Section: Introductionmentioning

confidence: 99%

A General Zero Attraction Proportionate Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

Wang

Albu

et al. 2017

Symmetry

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Abstract:A general zero attraction (GZA) proportionate normalized maximum correntropy criterion (GZA-PNMCC) algorithm is devised and presented on the basis of the proportionate-type adaptive filter techniques and zero attracting theory to highly improve the sparse system estimation behavior of the classical MCC algorithm within the framework of the sparse system identifications. The newly-developed GZA-PNMCC algorithm is carried out by introducing a parameter adjusting function into the cost function of the typical proportionate normalized maximum correntropy criterion (PNMCC) to create a zero attraction term. The developed optimization framework unifies the derivation of the zero attraction-based PNMCC algorithms. The developed GZA-PNMCC algorithm further exploits the impulsive response sparsity in comparison with the proportionate-type-based NMCC algorithm due to the GZA zero attraction. The superior performance of the GZA-PNMCC algorithm for estimating a sparse system in a non-Gaussian noise environment is proven by simulations.

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A Fast Converging Algorithm for Network Echo Cancelation

Cited by 28 publications

References 3 publications

Adaptive combination of IPNLMS filters for robust sparse echo cancellation

Adaptive combination of IPNLMS filters for robust sparse echo cancellation

Multirate Algorithm for Updating the Coefficients of Adaptive Filter

A General Zero Attraction Proportionate Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

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