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Model-Agnostic Meta-Learning Initialization for Distributed Multichannel Active Noise Control

Year: 2026

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2607.29117

Authors: Shen, Xiaoyi, Ji, Junwei, Gan, Woon-Seng, Shi, Dongyuan, Yang, Jun

Institutions: Institute of Acoustics, Nanyang Technological University, Northwestern Polytechnical University, University of Chinese Academy of Sciences

Topic: Advanced Adaptive Filtering Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Chinese Academy of Sciences

OpenAlex ID: W7172339963

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