Frequency-Domain Weighted FxLMS Algorithm for Feedback Active Noise Control

In this paper, we propose the frequency-domain weighted FxLMS algorithm for feedback active noise control. This algorithm aims to resolve the slow convergence issue of the conventional FxLMS algorithms by integrating a frequency-weighted method. This method dynamically adjusts weights based on the amplitude-frequency characteristics of narrowband noise, thereby improving tracking performance for time-varying narrowband noise. Through simulation experiments, we reveal that the FD-WFxNLMS algorithm achieves fast convergence, outperforming the conventional algorithms in feedback ANC systems.

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Publication Details

Journal
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences
Published
2024-08-26
DOI
https://doi.org/10.1587/transfun.2024smp0008
Primary Topic
Advanced Adaptive Filtering Techniques
Type
article
Field-Weighted Citation Impact
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article

Frequency-Domain Weighted FxLMS Algorithm for Feedback Active Noise Control

Tetsuya Shimamura, Ryota Noguchi, Yosuke Sugiura
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences
Advanced Adaptive Filtering Techniques
article

Frequency-Domain Weighted FxLMS Algorithm for Feedback Active Noise Control

Tetsuya Shimamura, Ryota Noguchi, Yosuke Sugiura
article en

Abstract

In this paper, we propose the frequency-domain weighted FxLMS algorithm for feedback active noise control. This algorithm aims to resolve the slow convergence issue of the conventional FxLMS algorithms by integrating a frequency-weighted method. This method dynamically adjusts weights based on the amplitude-frequency characteristics of narrowband noise, thereby improving tracking performance for time-varying narrowband noise. Through simulation experiments, we reveal that the FD-WFxNLMS algorithm achieves fast convergence, outperforming the conventional algorithms in feedback ANC systems.

IEICE Transactions on Fundamentals of Electronics Communications and Computer SciencesVol. E108.A(3)
Saitama University (JP)
Affordable and clean energy
Openalex Percentile: Top 85%
Advanced Adaptive Filtering Techniques
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