Review on Active Noise Control Technology for α-Stable Distribution Impulsive Noise

Chen, Shuming; Gu, Feihong; Liang, Chao; Meng, Hao; Wu, Kaiming; Zhou, Zhengdao

doi:10.1007/s00034-021-01814-6

Cited by 14 publications

(4 citation statements)

References 103 publications

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“…S YSTEM identification is one of the significant topics of signal processing which is widely used in many areas including acoustic echo cancellation [1]- [3], active noise control [4], acoustic channel equalization [5], source separation [6], and wireless multipath channels [7].…”

Section: Introductionmentioning

confidence: 99%

A Variable Step-Size Regularization-Based Quasi-Newton Adaptive Filter for System Identification

Bekrani,

Zayyani

2024

IEEE Access

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The family of least mean square (LMS) based adaptive filtering algorithms suffers from convergence performance limitation due to the sensitivity of such algorithms to the eigenvalue spread of the input correlation matrix. The quasi-Newton family of adaptive filtering algorithms addresses this limitation, but its performance is restricted by the estimation accuracy of the correlation matrix inverse, especially for highly correlated input signals. Furthermore, the convergence rate and the steady-state performance of both LMS and quasi-Newton families are thoroughly depending on their step-sizes. In this paper, a variable stepsize regularized quasi-Newton adaptive algorithm is proposed in the context of system identification. In this algorithm, inspired by the matrix inversion lemma, a modified regularized matrix inverse with a time-varying regularization is computed such that during the convergence, the contribution of matrix inverse in the weight update is reduced, resulting in a more noise-robust algorithm. The paper further provides a convergence analysis of the proposed quasi-Newton algorithm, wherein a variable step-size is proposed to achieve a high initial convergence rate and a low steady-state error in the context of system identification applications.

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Section: Introductionmentioning

confidence: 99%

A Variable Step-Size Regularization-Based Quasi-Newton Adaptive Filter for System Identification

Bekrani,

Zayyani

2024

IEEE Access

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“…8 When it deals with the impulsive noise, the attenuation performance have great limitations. 9 Many scholars have developed other ANC algorithms to improve the robustness of the FXLMS algorithm.…”

Section: Introductionmentioning

confidence: 99%

Maximum versoria criterion applied to hybrid active noise control algorithm for the impulsive noise

Zhang

Cheng

Chen

2023

Journal of Low Frequency Noise, Vibration and Active Control

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In the conventional hybrid active noise control (CHANC) algorithm, the filtered-x least mean square (FXLMS) algorithm is used in both feedforward and feedback structures. However, the FXLMS algorithm does not process the reference signal and the error signal before updating the weight vector, which leads to the inadequate robustness of the CHANC algorithm in the impulsive noise environment. To solve this problem, the maximum versoria criterion is incorporated into the HANC (MVC-HANC) algorithm in this paper. Firstly, the MVC-HANC algorithm employs a novel nonlinear function to compress the error signal. Secondly, the proposed algorithm uses the modified sigmoid function to constrain the reference signal. To further improve the noise attenuation performance, the MVC-HANC algorithm uses a novel exponential function to adjust the step-size adaptively. Simulation and experiment results demonstrate that the proposed algorithm has the better attenuation performance than the conventional HANC algorithm.

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“…α-stable distribution, which is famous for its heavy-tailed distribution and non-Gaussian model, is especially suitable for dealing with data involving extreme outliers. Thus, standard symmetric α-stable (SαS) distribution may be the most appropriate to describe impulsive noise [31,32].…”

Section: Introductionmentioning

confidence: 99%

Infinite Weighted p-Norm Sparse Iterative DOA Estimation via Acoustic Vector Sensor Array under Impulsive Noise

Liu,

Zhang,

Wang

et al. 2023

JMSE

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Recently, many direction-of-arrival (DOA) estimation techniques based on sparse representation have been proposed. However, these techniques often suffer from performance degradation issues in the presence of impulsive noise. This paper aims to overcome this challenge in conventional sparse-based techniques on an acoustic vector sensor array (AVSA). Firstly, to remove high outliers from the array output data, the output information of the AVSA is weighted by using the infinite norm. To further suppress outliers, a p-order cost function is formulated by extending the Frobenius norm to lower order, and then the expression of the signal power is quantified. Lastly, the DOA is approximated on the signal power by a spectral peak search mechanism. DOA estimation results based on Monte Carlo simulations validate the accuracy and robustness of the proposed techniques herein compared to the current, available methods in the context of intense impulsive noise, low generalized signal–to–noise ratio (GSNR), and a smaller number of snapshots.

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Review on Active Noise Control Technology for α-Stable Distribution Impulsive Noise

Cited by 14 publications

References 103 publications

A Variable Step-Size Regularization-Based Quasi-Newton Adaptive Filter for System Identification

A Variable Step-Size Regularization-Based Quasi-Newton Adaptive Filter for System Identification

Maximum versoria criterion applied to hybrid active noise control algorithm for the impulsive noise

Infinite Weighted p-Norm Sparse Iterative DOA Estimation via Acoustic Vector Sensor Array under Impulsive Noise

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