Data-reusing-based filtered-reference adaptive algorithms for active control of impulsive noise sources

Muhammad Tahir Akhtar, Akinori Nishihara

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

This paper deals with the adaptive algorithms for active noise control (ANC) systems being employed for the impulsive noise sources. The standard filtered-x least mean square (FxLMS) algorithm; based on the minimization of the variance of the error signal; is well suited for attenuation of Gaussian noise sources. For the impulsive noise; modeled as a stable non-Gaussian process; however, the second order moments do not exist and hence the FxLMS algorithm becomes unstable. The filtered-x least mean p-power (FxLMP) algorithm - based on minimizing the fractional lower order moment (FLOM) - gives robust performance for impulsive ANC; however, its convergence speed is very slow. This paper proposes two data-reusing (DR)-based adaptive algorithms for impulsive ANC. The Proposed-I DR algorithm is based on the normalized step-size FxLMS (NSS-FxLMS) algorithm, and the Proposed-II DR algorithm is based on the Author's recently proposed NSS generalized FxLMP (NSS-GFxLMP) algorithm. Extensive simulations are carried out, which demonstrate the effectiveness of the proposed algorithms in comparison with the existing algorithms.

Original languageEnglish
Pages (from-to)18-26
Number of pages9
JournalApplied Acoustics
Volume92
DOIs
Publication statusPublished - Jan 1 2015

Fingerprint

active control
moments
error signals
random noise
attenuation
optimization

Keywords

  • Active noise control
  • Data-reusing
  • Fractional lower order moment (FLOM)
  • FxLMS algorithm
  • Normalized step-size
  • Stable processes

ASJC Scopus subject areas

  • Acoustics and Ultrasonics

Cite this

Data-reusing-based filtered-reference adaptive algorithms for active control of impulsive noise sources. / Akhtar, Muhammad Tahir; Nishihara, Akinori.

In: Applied Acoustics, Vol. 92, 01.01.2015, p. 18-26.

Research output: Contribution to journalArticle

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