Data-Driven Method to Accelerate Convergence of Adaptive Hybrid Active Noise Control: Two-Stage Model-Agnostic Meta-Learning

Hybrid active noise control (ANC) is widely employed in portable commercial products to attenuate both broadband and narrowband noise. Although the adaptive hybrid ANC, updated by the filtered reference least mean square (FxLMS) algorithm, can achieve optimal noise control even with uncorrelated noise, its slow convergence speed significantly decreases dynamic noise reduction performance. To address this challenge, we propose a two-stage model-agnostic meta-learning (MAML) approach to compute the optimal initial coefficients of the control filters for the hybrid ANC, effectively reducing the convergence time of adaptive algorithms. Different from conventional variable step-size strategies, this data-driven method determines the optimal initial coefficients based on the statistical characteristics of the noise, ensuring system stability. Furthermore, numerical simulations demonstrate that two-stage MAML initialization of adaptive hybrid ANC significantly accelerates convergence speed for attenuating broadband and real-world noise.