$$L_{0}$$ L 0 -norm constraint normalized logarithmic subband adaptive filter algorithm

Shen, Zijie; Huang, Tianmin; Zhou, Kun

doi:10.1007/s11760-017-1230-4

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…For the purpose of decreasing steady-state error and speeding up the convergence rate of the SSAF algorithm, variable regularization parameter SSAF (VRP-SSAF) [12], some variable step-size SSAF algorithms [14,15], and affine projection SSAF [16,17] have been proposed. Nowadays many researchers have demonstrated that making full use of the saturation property of the error nonlinearities can gain splendid robustness against impulsive interferences, such as normalized logarithmic SAF (NLSAF) [18], arctangentbased NSAF algorithms (Arc-NSAFs) [19], maximum correntropy criterion (MCC) [20], the adaptive algorithms based on the step-size scaler (SSS) [21,22], and based on sigmoid function [23,24], and M-estimate based subband adaptive filter algorithm [25].…”

Section: Introductionmentioning

confidence: 99%

Robust Normalized Subband Adaptive Filter Algorithm with a Sigmoid-Function-Based Step-Size Scaler and Its Convex Combination Version

Shen

Tang

Yang

2021

Mathematical Problems in Engineering

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In this paper, by inserting the logarithm cost function of the normalized subband adaptive filter algorithm with the step-size scaler (SSS-NSAF) into the sigmoid function structure, the proposed sigmoid-function-based SSS-NSAF algorithm yields improved robustness against impulsive interferences and lower steady-state error. In order to identify sparse impulse response further, a series of sparsity-aware algorithms, including the sigmoid L0 norm constraint SSS-NSAF (SL0-SSS-NSAF), sigmoid step-size scaler improved proportionate NSAF (S-SSS-IPNSAF), and sigmoid L0 norm constraint step-size scaler improved proportionate NSAF (SL0-SSS-IPNSAF), is derived by inserting the logarithm cost function into the sigmoid function structure as well as the L0 norm of the weight coefficient vector to act as a new cost function. Since the use of the fix step size in the proposed SL0-SSS-IPNSAF algorithm, it needs to make a trade-off between fast convergence rate and low steady-state error. Thus, the convex combination version of the SL0-SSS-IPNSAF (CSL0-SSS-IPNSAF) algorithm is proposed. Simulations in acoustic echo cancellation (AEC) scenario have justified the improved performance of these proposed algorithms in impulsive interference environments and even in the impulsive interference-free condition.

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Section: Introductionmentioning

confidence: 99%

Robust Normalized Subband Adaptive Filter Algorithm with a Sigmoid-Function-Based Step-Size Scaler and Its Convex Combination Version

Shen

Tang

Yang

2021

Mathematical Problems in Engineering

Self Cite

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“…In order to favor such sparsity, the sparsity-aware technique is popular in adaptive filtering algorithms [36], [37], [38], [39] that adds the sparse constraint term in the original cost function. In the survey of robust SAF against impulsive noise, sparsity-aware approaches were only incorporated straightforwardly into the SSAF [40] and normalized logarithmic SAF [41] algorithms, and have not been analyzed theoretically yet. Also, the resulting algorithms require properly choosing the sparsity penalty parameter in a trial and error way, thereby limiting their usefulness.…”

Section: Introductionmentioning

confidence: 99%

Sparsity-Aware SSAF Algorithm with Individual Weighting Factors for Acoustic Echo Cancellation

Yu,

Yang,

Chen

et al. 2020

Preprint

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In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to enhance the S-IWF-SSAF performance in terms of convergence rate and steady-state error. A theoretical analysis shows that the S-IWF-SSAF algorithm outperforms the previous sign subband adaptive filtering with individual weighting factors (IWF-SSAF) algorithm in sparse scenarios. In particular, compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results. Simulations in both system identification and AEC situations have demonstrated our theoretical analysis and the effectiveness of the proposed algorithms.

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Improved NSAF Algorithms with Variable Control Parameter Against Impulsive Noises

Shen

Huang

Yang

2019

Circuits Syst Signal Process

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$$L_{0}$$ L 0 -norm constraint normalized logarithmic subband adaptive filter algorithm

Cited by 9 publications

References 29 publications

Robust Normalized Subband Adaptive Filter Algorithm with a Sigmoid-Function-Based Step-Size Scaler and Its Convex Combination Version

Robust Normalized Subband Adaptive Filter Algorithm with a Sigmoid-Function-Based Step-Size Scaler and Its Convex Combination Version

Sparsity-Aware SSAF Algorithm with Individual Weighting Factors for Acoustic Echo Cancellation

Improved NSAF Algorithms with Variable Control Parameter Against Impulsive Noises

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