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

Abstract

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.