Reducing impulsive noise in active noise control systems using FxLMS algorithm based on soft thresholding techniques

• Published in: Analog integrated circuits and signal processing Vol. 123; no. 2; p. 30
• Main Authors: Saravanan, V., Santhiyakumari, N., Shanmuga Sundaram, P.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2025; Springer Nature B.V
• Subjects: Active noise control; Adaptability; Adaptive filters; Algorithms; Circuits and Systems; Control algorithms; Control systems; Efficiency; Electrical Engineering; Engineering; Methods; Noise control; Noise reduction; Signal processing; Signal quality; Signal to noise ratio; Signal,Image and Speech Processing; FxLMS algorithm; Impulsive noise; Soft thresholding; VFxLHT algorithm; Active noise control systems
• ISSN: 0925-1030; 1573-1979
• DOI: 10.1007/s10470-025-02380-6

Impulsive noise significantly impacts signal quality and system performance, necessitating effective methods for its reduction. This paper introduces two adaptive filtering techniques based on the FxLMS algorithm, designed to address this challenge. The first method employs dynamic input thresholding, incorporating gradient-based and SNR-driven adjustments to suppress impulsive noise while retaining essential signal components. The second method builds on this by introducing hybrid thresholding applied to both input signals and filter coefficients, supported by double error smoothing to improve stability and adaptability under varying noise conditions. To evaluate the proposed methods, a comparative analysis is conducted with the Variable FxLMS Hybrid Thresholding (VFxLHT) technique, considering metrics such as steady-state noise suppression and computational efficiency. The results demonstrate that the proposed methods perform reliably across diverse noise conditions, maintaining signal fidelity while efficiently utilizing computational resources. These methods are intended as practical solutions for applications where impulsive noise control is essential to ensure reliable system operation without excessive computational complexity.