CO-INITIALIZATION OF CONTROL FILTER AND SECONDARY PATH VIA META-LEARNING FOR ACTIVE NOISE CONTROL
Ziyi Yang, Li Rao, Zhengding Luo, Dongyuan Shi, Qirui Huang
Abstract
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.
BibTeX
@inproceedings{icassp2026_coinitialization,
title = {CO-INITIALIZATION OF CONTROL FILTER AND SECONDARY PATH VIA META-LEARNING FOR ACTIVE NOISE CONTROL},
author = {Ziyi Yang and Li Rao and Zhengding Luo and Dongyuan Shi and Qirui Huang},
booktitle = {ICASSP 2026},
year = {2026}
}