An Active Impulsive Noise Control Algorithm With Logarithmic Transformation

Wu, Lifu; He, Huiguang; Qiu, Xiaojun

doi:10.1109/tasl.2010.2061227

Cited by 89 publications

(61 citation statements)

References 10 publications

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“…The major advantage of the proposed scheme is that various nonlinear error transformation methods in NLMS [2][3][4][5][6][7] can be easily modified and then applied to (3). We specifically discuss a low computational complexity method inspired by a nonlinear transformation used in [4].…”

Section: Practical Nonlinear Error Scalar Matrix Apsa (Nesm-apsa)mentioning

confidence: 99%

“…Their convergence performance can be further improved by using the nonlinear transformation of the error signal [2][3][4]: the switched norm algorithm in the applications of system identification and acoustic echo cancellation [2], the logarithmic transformation in the context of active noise control [5][6][7], and the tan transformation tested in system identification [8] etc. Unfortunately their major drawbacks are performance degradation with correlated input signals.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear error scalar matrix improved affine projection sign algorithm

Zhao¹,

Huang²,

Zheng³

2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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Keywords: Affine projection sign algorithm; robust adaptive algorithm; errror scalar matrix.Abstract. Proposed is an affine projection sign algorithm with a nonlinear error scalar matrix to improve the robustness and the tracking performance against non-Gaussian impulsive interferences. The error scalar matrix scalars down the errors of some projection directions in the presence of impulsive noise. The major contribution of the letter is that variable error nonlinearity methods used in normalized least mean square (NLMS) can be applied to the scalar matrix with a little modification. An ideal scalar matrix is presented in the simulation environment of the two component Gaussian mixture noise model. Although a closed-form solution of the ideal matrix cannot be obtained in practice, it provides us a heuristic consideration about how to design the scalar matrix and theoretically best learning curves that the proposed method can achieve. We also discuss a practical method to approximate the optimal learning curve. Improved performance of the proposed algorithm is demonstrated in a system identification scenario.

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Section: Practical Nonlinear Error Scalar Matrix Apsa (Nesm-apsa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear error scalar matrix improved affine projection sign algorithm

Zhao¹,

Huang²,

Zheng³

2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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show abstract

“…And in consequence to that, no selection of the threshold parameters are required. Wu et al in [10] suggested a new technique (FxlogLMS) based on fair M-estimator that minimizes the squared logarithmic transformations of error signal to achieve robustness. However, the algorithm has the drawback of reaching a dead zone in the process of updating the filter coefficients.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive algorithm for active control of impulsive noise

Mirza

Zeb

Sheikh

2016

EURASIP J. Adv. Signal Process.

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Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. To overcome this limitation, new methods must be investigated. In this paper, we propose the filtered-x state-space recursive least square (FxSSRLS), an SSRLS-based practical and adaptive algorithm for ANC. Computer simulations are executed to verify the enhanced performance of the FxSSRLS algorithm. Symmetric α-stable (SαS) distributions are used to model impulsive noise. The results show that the proposed FxSSRLS algorithm is more robust in eliminating high-peaked impulses than the recently reported algorithms for ANC applications. Moreover, the suggested solution exhibits better stability and faster convergence, without jeopardizing the performance of the proposed solution in terms of residual noise suppression in the presence of impulses.

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“…This "logarithmic-order" class includes Į-stable noise process, which gives an increased number of outliers. More recently, Wu et al developed a filtered-x least-mean square with logarithmic transformation (FxLogLMS) algorithm which is more robust than existing algorithms for active noise control (ANC) in the presence of Į-stable noise [34]. In this paper, motivated by this framework and the FxLogLMS algorithm, a novel enhanced recursive least mean pth power algorithm with logarithmic transformation (RLogLMP) is proposed to improve the filtering performance of adaptive Volterra filters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive recursive algorithm with logarithmic transformation for nonlinear system identification in α -stable noise

Zhao

et al. 2015

Digital Signal Processing

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An Active Impulsive Noise Control Algorithm With Logarithmic Transformation

Cited by 89 publications

References 10 publications

Nonlinear error scalar matrix improved affine projection sign algorithm

Nonlinear error scalar matrix improved affine projection sign algorithm

Robust adaptive algorithm for active control of impulsive noise

Adaptive recursive algorithm with logarithmic transformation for nonlinear system identification in α -stable noise

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