Co-Initialization of Control Filter and Secondary Path via Meta-Learning for Active Noise Control

2026-01-20

AI Summary

This work addresses the poor initial performance of active noise control (ANC) systems under abrupt acoustic environment changes, which heavily depends on parameter initialization. For the first time, model-agnostic meta-learning (MAML) is introduced to this domain, enabling rapid adaptation by jointly optimizing the initial parameters of both the control filter and the secondary path model. The proposed method employs a two-stage inner-loop procedure that simulates system identification and noise cancellation, integrated within an FxLMS framework with online secondary path modeling. Notably, only a small amount of real-world path data is required during pre-training. Experimental results demonstrate that, compared to a non-reinitialized baseline, the approach significantly reduces early-stage error, shortens convergence time, lowers auxiliary noise energy, and achieves faster performance recovery following sudden path changes.

Technology Category

• Machine Learning: Active Learning
• Multiagent Systems: Multiagent Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Active Noise Control
• Initialization
• Secondary Path
• Environment Change
• Adaptation

Innovation

• meta-learning
• co-initialization
• active noise control
• FxLMS
• secondary path modeling