Improving robustness of filtered-x least mean p-power algorithm for active attenuation of standard symmetric-α-stable impulsive noise

Akhtar, Muhammad Tahir; Mitsuhashi, Wataru

doi:10.1016/j.apacoust.2011.02.009

Cited by 58 publications

(37 citation statements)

References 11 publications

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“…where Ef Á g denotes expectation or ensemble averaging of the quantity inside, and 9 In the proposed FxMGNLMP algorithm, the empirical constant b is chosen on an experimental basis for fast and stable convergence and is adjusted to b ¼ 0:75. The simulation parameters are summarized in Table 2.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…For b ¼ 1, the FxMGNLMP algorithm reduces to the FxMNLMP algorithm of Ref. 9. For clarity of presentation, the FLOM-based algorithms discussed in this paper are summarized in Table 1.…”

Section: Flom-based Algorithms For Anc Of Impulsive Noisementioning

confidence: 99%

“…Hereafter, the resulting algorithm is referred to as the filtered-x modified normalized least mean p-power (FxMNLMP) algorithm. 9 As a generalization to the NLMP update equation, the following update equation for the generalized normalized least mean p-power (GNLMP) is proposed: 10 wðn þ 1Þ ¼ wðnÞ þ lðnÞpðeðnÞÞ hai ðxðnÞÞ hðqÀ1Þai ;…”

Section: Flom-based Algorithms For Anc Of Impulsive Noisementioning

confidence: 99%

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Fractional lower order moment based adaptive algorithms for active noise control of impulsive noise sources

Akhtar

2012

The Journal of the Acoustical Society of America

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This letter deals with active noise control (ANC) for impulsive noise sources being modeled using non-Gaussian stable process. The filtered-x least mean square algorithm is based on minimization of the variance of the error signal and becomes unstable for impulsive noise. The filtered-x least mean p-power algorithm—based on minimizing the fractional lower order moment—gives a robust performance for impulsive ANC; however, its convergence speed is very slow. This letter proposes modifying and employing a generalized normalized LMP algorithm for impulsive ANC. Extensive simulations are carried out which demonstrate the effectiveness of the proposed algorithm.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

“…For b ¼ 1, the FxMGNLMP algorithm reduces to the FxMNLMP algorithm of Ref. 9. For clarity of presentation, the FLOM-based algorithms discussed in this paper are summarized in Table 1.…”

Section: Flom-based Algorithms For Anc Of Impulsive Noisementioning

confidence: 99%

Section: Flom-based Algorithms For Anc Of Impulsive Noisementioning

confidence: 99%

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Fractional lower order moment based adaptive algorithms for active noise control of impulsive noise sources

Akhtar

2012

The Journal of the Acoustical Society of America

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“…An outlier is one that appears to deviate markedly from other members of the sample in which it occurs [6]. Several M-estimators are available so far, such as Huber [12], Hampel [13], FXLMP [14], SUNS [15], modified SUNS [16], and Fair [6].…”

Section: Previously Applied Algorithmsmentioning

confidence: 99%

Application of reinforcement learning for active noise control

Sabzevari¹,

Moavenian²

2017

Turk J Elec Eng & Comp Sci

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Active noise control (ANC) systems are used to reduce the sound noise level by generating antinoise signals.M-Estimators are widely employed in ANC systems for updating the adaptive FIR filter taps used as the system controller.Observing the state-of-the-art M-estimators design shows that there is a need for further improvements. In this paper, a feedback ANC based on the reinforcement learning (RL) method is proposed. The sensitivity of the constant parameter in the RL method is checked. The effectiveness of the proposed method is proven by comparing the results with previous feedforward studies through computer simulations.

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“…Since in the literature the term impulsive noise is often used to indicate heavytailed distributed disturbances that affect the operations of adaptive ANC systems [9,10,11,12,13,14], in order not to generate ambiguities, we also term the primary source signal as highly nonstationary noise (HNN) and the proposed scheme as active HNN control (AHNNC) system.…”

Section: Introductionmentioning

confidence: 99%

Application of wave field synthesis to active control of highly non-stationary noise

et al. 2018

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Improving robustness of filtered-x least mean p-power algorithm for active attenuation of standard symmetric-α-stable impulsive noise

Cited by 58 publications

References 11 publications

Fractional lower order moment based adaptive algorithms for active noise control of impulsive noise sources

Fractional lower order moment based adaptive algorithms for active noise control of impulsive noise sources

Application of reinforcement learning for active noise control

Application of wave field synthesis to active control of highly non-stationary noise

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