works on

From the 1 of 10 linked papers with an AI index.

activity
20242026
collaborators

10 papers

eess.AS2026

Deep Learning-Based Active Trim Panels for Enhanced Aircraft Interior Noise Control

Boxiang Wang, Malte Misol, Zhengding Luo +4

Active noise control (ANC) trim panels offer an effective solution to suppress multi-tonal noise in aircraft. The selective fixed-filter ANC (SFANC) method, characterized by low co…

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

Predictive Directional Selective Fixed-Filter Active Noise Control for Moving Sources via a Convolutional Recurrent Neural Network

Boxiang Wang, Zhengding Luo, Dongyuan Shi +3

Directional Selective Fixed-Filter Active Noise Control (D-SFANC) can effectively attenuate noise from different directions by selecting the suitable pre-trained control filter bas…

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

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…