A two adaptive filters-based method for reducing effects of acoustic feedback in single-channel feedforward ANC systems

Muhammad Tufail, Shakeel Ahmed, Muhammad Rehan, Muhammad Tahir Akhtar

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

In most of the existing approaches for neutralizing the effects of acoustic feedback in feed-forward active noise control (FF-ANC) systems, three adaptive filters have been employed. Besides the noise control and feedback neutralization filters (FBNF), a third disturbance removal filter has been exploited to obtain suitable signals for (apposite) adaptation of the other two (adaptive filters). In this paper, we develop an effective acoustic feedback neutralization method which does not require any supplementary third adaptive filter. The proposed method incorporates a probe signal with a time-varying gain and a novel variable step-size strategy for adaptation of the FBNF. During the transient state, the FBNF is excited by a large variance probe signal and a large valued step-size. This enhances the convergence rate of the FBNF. As the FF-ANC system converges, the level of probe noise is decreased to achieve good residual error attenuation performance (at steady-state). Furthermore, the step-size is also reduced to a small value to improve the steady-state performance of the acoustic FBNF. Simulations results show that the proposed two adaptive filters-based method outperforms the existing methods for broad-band reference noise sources and yields better steady-state residual error attenuation performance for narrow-band noise signals.

Original languageEnglish
Pages (from-to)18-27
Number of pages10
JournalDigital Signal Processing: A Review Journal
Volume90
DOIs
Publication statusPublished - Jul 2019

Keywords

  • Acoustic feedback neutralization
  • Active noise control
  • Gain scheduling
  • Probe signal
  • Variable step-size

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Applied Mathematics

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