A Practical Data-Driven Step-Size Selection Method for Adaptive Active Noise Control Based on Modified Meta-Learning

Active noise control (ANC) is widely recognized as an effective and efficient solution for attenuating urban noise. Least mean square (LMS)-based adaptive algorithms, particularly the filtered-reference LMS (FxLMS) algorithm, play a central role in adaptive ANC systems due to their computational efficiency and optional steady-state performance. However, their effectiveness heavily depends on appropriate step-size selection. An unsuitable step size can severely degrade convergence speed and stability. Traditional step-size strategies, such as variable step-size approaches, often involve high computational complexity and are limited to specific noise types. To address this, this letter proposes a data-driven step-size selection method for the FxLMS algorithm based on modified model-agnostic meta-learning (MAML), incorporating a forgetting factor to mitigate the filter's initial zero effect. Compared to conventional methods, the proposed approach can determine an optimal step size across various noise types without requiring additional computations during control, making it highly suitable for practical deployment. Numerical simulations using real-world paths and noise further verify its effectiveness.