Robust variable step-size sign subband adaptive filter algorithm against impulsive noise

Wen, Pengwei; Zhang, Jiashu

doi:10.1016/j.sigpro.2017.04.012

Cited by 28 publications

(5 citation statements)

References 12 publications

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“…The impulsive noise ζ i is usually modelled as a Bernoulli-Gauss (BG) process, i.e. ζ i = ω i • ε i , where ω i is a Bernoulli process with the probability density function described by P(ω i = 1) = P and P(ω i = 0) = 1 − P, and ε i is a Gaussian process with zero mean and variance σ 2 ε = κ • σ 2 n , κ 1 and σ 2 ν = σ 2 n + Pσ 2 ε = (1 + Pκ)σ 2 n , where σ 2 n = 1 is the variance of zero-mean Gaussian noise and κ = 10 [13]. The results of filtering operation are evaluated by the geometric noise reduction factor (GNRF)…”

Section: Selection Of Number Of Overlapping Samplesmentioning

confidence: 99%

EEG signal improvement with cascaded filter based on OWA operator

Pander

2019

SIViP

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Noise reduction methods have a great impact on the performance of all EEG signal processing systems. The work involves the reduction in impulsive interferences. It is impossible to suppress effectively such a noise using linear filtering approach. The work presents the properties of a cascaded filter based on the ordered weighted aggregation (OWA) operator and its application to improve the electroencephalogram (EEG) signal. The OWA operator is a class of mean-like aggregation operators. By introducing a nonlinear sorting process and by assigning appropriate values of weights to the sorted samples, the improvement in filtering in the impulsive environment is achieved. The structure of the proposed cascade consists of two layers. The first layer contains two OWA filters that share a certain number of signal samples. The second layer averages the outputs of the first layer. The algorithm for the rise of filtering efficiency has been introduced. The performance of the new method has been experimentally compared with the traditional methods of impulsive noise reduction, using synthetic as well as real signals from the CHB-MIT database. The obtained results demonstrate that in the field of impulsive noise suppression and preprocessing, the proposed cascaded OWA filter brings about a significant improvement in the signal-to-noise ratio.

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Section: Selection Of Number Of Overlapping Samplesmentioning

confidence: 99%

EEG signal improvement with cascaded filter based on OWA operator

Pander

2019

SIViP

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“…e classical sign subband adaptive filter (SSAF) algorithm derived from L 1 -norm optimization criterion only uses the sign information of the subband error signal, thus obtaining superb capability of suppressing impulsive interference [12], while its weakness is a relatively higher steady-state error and a slower convergence rate [13]. 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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“…In addition, if the impulse response of the echo path is sparse, the convergence speed of SSAF will further decrease. In order to speed up the convergence rate of the algorithm, Ni, J et al proposed variable regularization parameter SSAF (VRP-SSAF) to further improve performance [10,11].…”

Section: Introductionmentioning

confidence: 99%

Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filter

Zhang

Geng

et al. 2021

Scientific Programming

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The proportionate affine projection sign subband adaptive filter (PAP-SSAF) has a better performance than the affine projection sign subband adaptive filter (AP-SSAF) when we eliminate the echoes. Still, the robustness of the PAP-SSAF algorithm is insufficient under unknown environmental conditions. Besides, the best balance remains to be found between low steady-state misalignment and fast convergence rate. In order to solve this problem, we propose a normalized combination of PAP-SSAF (NCPAP-SSAF) based on the normalized adaption schema. In this paper, a power normalization adaptive rule for mixing parameters is proposed to further improve the performance of the NCPAP-SSAF algorithm. By using Nesterov’s accelerated gradient (NAG) method, the mixing parameter of the control combination can be obtained with less time consumed when we take the l1-norm of the subband error as the cost function. We also test the algorithmic complexity and memory requirements to illustrate the rationality of our method. In brief, our study contributes a novel adaptive filter algorithm, accelerating the convergence speed, reducing the steady-state error, and improving the robustness. Thus, the proposed method can be utilized to improve the performance of echo cancellation. We will optimize the combination structure and simplify unnecessary calculations to reduce the algorithm’s computational complexity in future research.

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Robust variable step-size sign subband adaptive filter algorithm against impulsive noise

Cited by 28 publications

References 12 publications

EEG signal improvement with cascaded filter based on OWA operator

EEG signal improvement with cascaded filter based on OWA operator

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

Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filter

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