Active noise control (ANC) must adapt quickly when the acoustic environment changes, yet early performance is largely dictated by initialization. We address this with a Model-Agnostic Meta-Learning (MAML) co-initialization that jointly sets the control filter and the secondary-path model for FxLMS-based ANC while keeping the runtime algorithm unchanged. The initializer is pre-trained on a small set of measured paths using short two-phase inner loops that mimic identification followed by residual-noise reduction, and is applied by simply setting the learned initial coefficients. In an online secondary path modeling FxLMS testbed, it yields lower early-stage error, shorter time-to-target, reduced auxiliary-noise energy, and faster recovery after path changes than a baseline without re-initialization. The method provides a simple fast start for feedforward ANC under environment changes, requiring a small set of paths to pre-train.

翻译：主动噪声控制（ANC）系统在声学环境变化时必须快速适应，而早期性能很大程度上取决于初始化过程。本文提出一种基于模型无关元学习（MAML）的协同初始化方法，在保持运行时算法不变的前提下，为基于FxLMS的ANC系统联合设置控制滤波器与次级路径模型。该初始化器通过模拟“辨识-残差噪声抑制”两阶段过程的短时内循环，在小规模实测路径数据集上进行预训练，应用时仅需载入学习得到的初始系数即可。在采用在线次级路径建模的FxLMS测试平台上，相比无重新初始化的基线方法，本方案实现了更低的早期误差、更短的达标时间、更少的辅助噪声能量以及路径变化后更快的恢复速度。该方法为前馈式ANC系统在环境变化下提供了一种简单快速的启动方案，且仅需少量路径数据进行预训练。