An efficient, fast converging adaptive filter for network echo cancellation

Grant, Steven

doi:10.1109/acssc.1998.750893

Cited by 155 publications

(100 citation statements)

References 2 publications

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“…Based on [5], PNLMS++ is an adaptive algorithm which alternate the coefficient update between PNLMS and NLMS. correspondingly, this concept could be extended to proportionate NSAF structure and the weight vector update expected to alternate between PNSAF and NSAF updating equation.…”

Section: A Algorithm Ii: Proportinate Nsaf ++(Pnsaf++)mentioning

confidence: 99%

“…But unfortunately, it slows down after initial fast convergence, and also this algorithm has poor performance in dispersive channels. In [5], the PNLMS++ algorithm was proposed to alternate the PNLMS and NLMS algorithms during the adaptation. This strategy leads to fast initial convergence and at least as fast convergence as the NLMS algorithm later on.…”

Section: Introductionmentioning

confidence: 99%

“…In fact this algorithm alternate the coefficient update between PNLMS and NLMS and gave similar performance to PNLMS for sparse systems but with better robustness in particular to echo path change [5]. Updating rules of PNLMS++ are based on switching between PNLMS and NLMS weight vector equations with n as index parameter.…”

Section: Introductionmentioning

confidence: 99%

“…Updating rules of PNLMS++ are based on switching between PNLMS and NLMS weight vector equations with n as index parameter. Consequently, PNLMS and NLMS are used alternately at odd and even sample instants n , respectively [5], [7].…”

Section: Introductionmentioning

confidence: 99%

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The Novel Proportionate Normalized Subband Adaptive Filter Algorithms for Sparse System Identification

Abadi¹,

Kadkhodazadeh²

2012

IJCEE

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Abstract-System identification is one of the most important applications of adaptive filter algorithms. Some systems such as echo response that must be identified by the echo canceller have sparse nature. Classic adaptive filter algorithms like LMS or NLMS have poor performance in this way. Proportionate NLMS (PNLMS) algorithm has been developed to make improve poorly performance of NLMS in system identification, unfortunately it suffers from slow down during adaption process. To solve this problem, the concept of proportionate adaptation is extended to the normalized subband adaptive filter (NSAF), and three types proportionate normalized subband adaptive filter (PNSAF) algorithms are established in this paper. Proposed algorithms are proportionate normalized subband adaptive filter ++ (PNSAF++), the set-membership PNSAF (SM-PNSAF) and the set-membership PNSAF++ (SM-PNSAF++). Here we demonstrate that PNSAF++ algorithm improve the convergence rate of PNSAF in sparse channels. The SM-PNSAF and SM-PNSAF++ also exhibit good performance with significant reduction in the overall computational complexity compared with the ordinary PNSAF. The simulation results show good performance of the proposed algorithms.Index Terms-Proportionate normalized subband adaptive filter, set-membership, sparse system identification

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Section: A Algorithm Ii: Proportinate Nsaf ++(Pnsaf++)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Novel Proportionate Normalized Subband Adaptive Filter Algorithms for Sparse System Identification

Abadi¹,

Kadkhodazadeh²

2012

IJCEE

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“…Furthermore, for time-varying systems, it can be seen that PNLMS will perform poorly if the trajectory of a coefficient is required to track through or close to zero since the adaptation gain for that coefficient will become inappropriately small. In [13], it was shown that alternating the coefficient update between PNLMS and NLMS gave similar performance to PNLMS for sparse systems but with better robustness in particular to echo path change. The resulting algorithm, known as PNLMSþþ, can be used with various switching schemes and good results have been presented when PNLMS and NLMS are used alternately at odd and even sample instants n, respectively.…”

Section: Pnlmsþþmentioning

confidence: 99%

Adaptive algorithms for sparse echo cancellation

2006

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The cancellation of echoes is a vital component of telephony networks. In some cases the echo response that must be identified by the echo canceller is sparse, as for example when telephony traffic is routed over networks with unknown delay such as packet-switched networks. The sparse nature of such a response causes standard adaptive algorithms including normalized LMS to perform poorly. This paper begins by providing a review of techniques that aim to give improved echo cancellation performance when the echo response is sparse. In addition, adaptive filters can also be designed to exploit sparseness in the input signal by using partial update procedures. This concept is discussed and the MMax procedure is reviewed. We proceed to present a new high performance sparse adaptive algorithm and provide comparative echo cancellation results to show the relative performance of the existing and new algorithms. Finally, an efficient low cost implementation of our new algorithm using partial update adaptation is presented and evaluated. This algorithm exploits both sparseness of the echo response and also sparseness of the input signal in order to achieve high performance without high computational cost. r

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Set‐theoretic adaptive filtering based on data‐driven sparsification

Yukawa¹,

Yamada²

2011

Adaptive Control & Signal

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In this article, we propose a fast and efficient algorithm named the adaptive parallel Krylov-metric projection algorithm. The proposed algorithm is derived from the variable-metric adaptive projected subgradient method, which has recently been presented as a unified analytic tool for various adaptive filtering algorithms. The proposed algorithm features parallel projection-in a variable-metric sense-onto multiple closed convex sets containing the optimal filter with high probability. The metric is designed based on (i) sparsification by means of a certain data-dependent Krylov subspace and (ii) maximal use of the obtained sparse structure for fast convergence. The numerical examples show the advantages of the proposed algorithm over the existing ones in stationary/nonstationary environments.

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An efficient, fast converging adaptive filter for network echo cancellation

Cited by 155 publications

References 2 publications

The Novel Proportionate Normalized Subband Adaptive Filter Algorithms for Sparse System Identification

The Novel Proportionate Normalized Subband Adaptive Filter Algorithms for Sparse System Identification

Adaptive algorithms for sparse echo cancellation

Set‐theoretic adaptive filtering based on data‐driven sparsification

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