用元学习优化降噪系统初始值，提升多节点降噪收敛速度。
Model-Agnostic Meta-Learning Initialization for Distributed Multichannel Active Noise Control
- 基于元学习的初始化策略，适配不同节点声学特性。
- 在宽带与真实噪声下收敛速度显著加快，降噪效果更好。
- 适合大规模分布式主动降噪系统，提升协同效率。
分布式多通道主动降噪（DMCANC）作为一种可扩展的大面积降噪框架，通过多个节点运行本地单通道降噪控制器并交换关键信息实现全局控制。现有实现主要依赖零或随机初始化，导致自适应滤波器收敛缓慢，限制了节点间协作效率。本文提出一种基于模型无关元学习（MAML）的初始化策略，通过聚合各节点的异质声学特性（包括主路径和次级路径），训练一个能跨分布式降噪系统有效泛化的初始参数。该初始化方案部署至所有节点后，在平稳与时变噪声条件下均显著提升了收敛速度。数值仿真在宽带噪声和真实噪声场景下验证了所提方法相较于传统DMCANC具有更快的收敛性和更优的降噪性能，凸显了MAML初始化在大规模主动降噪中的潜力。
原文摘要 · Abstract (English)
Distributed multichannel active noise control (DMCANC) has emerged as a scalable framework for large-area noise reduction, where multiple nodes operate local single-channel ANC controllers and exchange essential information to achieve global control. A key limitation of existing DMCANC implementations lies in their reliance on zero or random initialization, which leads to slow convergence of adaptive filters and restricts the efficiency of internode collaboration. To address this issue, this paper introduces a model-agnostic meta-learning (MAML) based initialization strategy for DMCANC. By aggregating heterogeneous acoustic characteristics across nodes-ncluding primary and secondary paths-a MAML framework is trained to learn an initialization that generalizes effectively across distributed ANC systems. The MAML initialization is then deployed to all nodes to improve convergence speed under both stationary and time-varying noise conditions. Numerical simulations applied on broadband and real-world noise demonstrate that the proposed algorithms achieves substantially faster convergence and improved noise reduction performance compared with conventional DMCANC, highlighting the potential of MAML initialization as an effective method for large-scale ANC.
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