AI 中文总结
针对分布式多通道主动噪声控制依赖零/随机初始化导致收敛慢的问题，提出基于模型无关元学习的初始化策略，经仿真验证可提升收敛速度与降噪性能。
AI 中文摘要
分布式多通道主动噪声控制（DMCANC）已成为适用于大面积降噪的可扩展框架，该框架中多个节点运行本地单通道主动噪声控制（ANC）控制器，并交换关键信息以实现全局控制。现有DMCANC实现的一个关键局限在于依赖零初始化或随机初始化，这会导致自适应滤波器收敛缓慢，并限制节点间协作效率。为解决该问题，本文提出一种基于模型无关元学习（MAML）的DMCANC初始化策略。通过聚合节点间的异质声学特性（包括主路径和次路径），训练MAML框架以学习可在分布式ANC系统间有效泛化的初始化参数。随后将该MAML初始化部署到所有节点，以提升平稳噪声和时变噪声条件下的收敛速度。针对宽带噪声和真实世界噪声的数值模拟表明，与传统DMCANC相比，所提算法实现了显著更快的收敛速度和更优的降噪性能，凸显了MAML初始化作为大规模ANC有效方法的潜力。
英文摘要
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.
