English

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

Audio and Speech Processing 2026-04-28 v1 Sound

Abstract

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 sufficient adaptation. In contrast, the Generative Fixed-Filter Active Noise Control (GFANC) method offers fast response speed, but its lack of adaptability may lead to large steady-state errors. This paper proposes a hybrid GFANC-FxNLMS algorithm to leverage the complementary advantages of both approaches. In the hybrid GFANC-FxNLMS algorithm, GFANC provides a frame-level control filter as an initialization for FxNLMS, while FxNLMS performs continuous adaptation at the sampling rate. Small variations in the GFANC-generated filter may repeatedly reinitialize FxNLMS, interrupting its adaptation process and destabilizing the system. An online clustering module is introduced to avoid unnecessary re-initializations and improve system stability. Simulation results show that the proposed algorithm achieves fast response, very low steady-state error, and high stability, requiring only one pre-trained broadband filter.

Keywords

Cite

@article{arxiv.2601.15889,
  title  = {A Stabilized Hybrid Active Noise Control Algorithm of GFANC and FxNLMS with Online Clustering},
  author = {Zhengding Luo and Haozhe Ma and Boxiang Wang and Ziyi Yang and Dongyuan Shi and Woon-Seng Gan},
  journal= {arXiv preprint arXiv:2601.15889},
  year   = {2026}
}

Comments

Accepted by 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2026)