With the advent of efficient low-cost processors and electroacoustic components, there is renewed interest in the practical implementation of active noise control (ANC). However, the slow convergence of conventional adaptive algorithms deployed in ANC restricts its handling of typical amplitude-varying noise. Hence, we proposed a modified model-agnostic, meta-learning (MAML) strategy to obtain an initial control filter, which accelerates an adaptive algorithm's convergence when dealing with different types of amplitude-varying low-frequency noise. Numerical simulations with measured paths and real noise sources demonstrate its convergence acceleration efficacy in practical scenarios.

中文翻译：

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基于改进的模型不可知元学习算法的快速自适应主动噪声控制

随着高效低成本处理器和电声组件的问世，人们对有源噪声控制（ANC）的实际实施有了新的兴趣。但是，部署在ANC中的常规自适应算法的缓慢收敛限制了其对典型幅度变化噪声的处理。因此，我们提出了一种与模型无关的改进型元学习（MAML）策略，以获得初始控制滤波器，该滤波器可在处理不同类型的振幅变化的低频噪声时加快自适应算法的收敛速度。在实际情况下，通过测量路径和实际噪声源进行的数值模拟证明了其收敛加速功效。