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

TL;DR

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.

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.

Figures (6)

• Figure 1: Block diagram of a feedforward active noise control system
• Figure 2: Equivalent block diagram of FxLMS at the initial $t$th iteration
• Figure 3: Learned step size over time with different $\alpha$ values.
• Figure 4: Broadband noise reduction performance of FxLMS with different step-size strategies: (a) the error signal and (b) the average noise reduction level in every 0.5 s.
• Figure 5: Average noise reduction level in every 0.5 s for various real-world noise, using FxLMS with different step-size strategies.