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

TL;DR

This paper introduces a hybrid active noise control algorithm combining GFANC and FxNLMS with online clustering to enhance stability, response speed, and steady-state accuracy in noise cancellation systems.

Contribution

It proposes a novel hybrid GFANC-FxNLMS algorithm with an online clustering module to prevent destabilization caused by reinitializations, improving performance over existing methods.

Findings

Achieves fast response and low steady-state error.

Maintains high stability with only one pre-trained filter.

Outperforms individual GFANC or FxNLMS in simulations.

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.