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From the 1 of 11 linked papers with an AI index.

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11 papers

eess.AS2026

Adaptive Momentum Enhanced Distributed Multichannel Active Noise Control for Faster Convergence under Communication Delays

Junwei Ji, Woon-Seng Gan, Boxiang Wang +2

Distributed multichannel active noise control (DMCANC) reduces the computational burden of centralized ANC systems by distributing processing tasks across multiple nodes, while req…

eess.AS2026

Spatial-Frequency Cued Generative Fixed-Filter Active Noise Control Based on Deep Learning in Reverberant Environments

Boxiang Wang, Haowen Li, Dongyuan Shi +4

The paper introduces a spatial‑frequency cued generative fixed‑filter active noise control (SF‑GFANC) system that uses a multi‑task CRNN to estimate 3D source location and filter c…

eess.AS2026

Transformer-based End-to-End Control Filter Generation for Active Noise Control

Ziyi Yang, Zhengding Luo, Yisong Zou +3

To address the limitations of existing Generative Fixed-Filter Active Noise Control (GFANC) methods, which rely on filter decomposition and recombination and require supervised lea…

eess.AS2026

A Stabilized Hybrid Active Noise Control Algorithm of GFANC and FxNLMS with Online Clustering

Zhengding Luo, Haozhe Ma, Boxiang Wang +3

The Filtered-x Normalized Least Mean Square (FxNLMS) algorithm suffers from slow convergence and a risk of divergence, although it can achieve low steady-state errors after suffici…

eess.AS2026

Distributed Multichannel Active Noise Control with Asynchronous Communication

Junwei Ji, Dongyuan Shi, Boxiang Wang +3

Distributed multichannel active noise control (DMCANC) offers effective noise reduction across large spatial areas by distributing the computational load of centralized control to…

eess.AS2026

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

Ziyi Yang, Li Rao, Zhengding Luo +3

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-Agnos…