用元学习联合初始化降噪滤波器与次路径模型，提升环境变化时的快速响应能力。
Co-Initialization of Control Filter and Secondary Path via Meta-Learning for Active Noise Control
- 采用元学习联合优化控制滤波器与次路径模型的初始参数
- 早期误差更低，收敛更快，路径突变后恢复更迅速
- 仅需少量实测路径即可预训练，适合实时主动降噪场景
主动降噪（ANC）在声学环境变化时需快速适应，但初期性能很大程度上取决于初始设置。本文提出一种模型无关的元学习（MAML）联合初始化方法，同时设定基于FxLMS的ANC系统的控制滤波器与次路径模型，而运行时算法保持不变。该初始化器在少量实测路径上通过短时双阶段内循环（模拟路径辨识与残余噪声抑制）进行预训练，并通过直接设置学习到的初始系数来应用。在在线次路径建模的FxLMS测试平台上，相比无重初始化基线，该方法显著降低早期误差、缩短达到目标时间、减少辅助噪声能量，并在路径变化后实现更快恢复。该方法为环境变化下的前馈主动降噪提供了一种简单高效的快速启动方案，仅需少量路径数据进行预训练。
原文摘要 · Abstract (English)
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.
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