A Novel Monte Carlo Gradient Method Based on Meta-learning for Effective Step-size Selection in Active Noise Control

TL;DR

This paper introduces a Monte Carlo gradient meta-learning method for adaptive step-size selection in active noise control, improving convergence and noise reduction without extra computational costs.

Contribution

It proposes a novel meta-learning approach that adaptively determines step sizes in ANC systems, enhancing performance and robustness over traditional methods.

Findings

Effective noise suppression demonstrated in simulations

Improved convergence speed over existing algorithms

No additional computational burden on FxLMS

Abstract

Active noise control (ANC) is an effective approach to noise suppression, and the filtered-reference least mean square (FxLMS) algorithm is a widely adopted method in ANC systems, owing to its computational efficiency and stable performance. However, its convergence speed and noise reduction performance are highly dependent on the step size parameter. Common step-size algorithms-such as normalized and variable step-size variants-require additional computational resources and exhibit limited adaptability under varying environmental conditions. To address this challenge, a novel Monte Carlo gradient meta-learning (MCGM) approach is proposed herein to determine an appropriate step size, into which a forgetting factor is incorporated to mitigate the impact of initial zero effect. Compared to other algorithms, the proposed method imposes no additional computational burden on FxLMS operations. Numerical simulations involving real-world acoustic paths and noise signals further confirm its effectiveness and robustness.