A Stabilized Hybrid Active Noise Control Algorithm of GFANC and FxNLMS with Online Clustering
For active noise control applications, this hybrid algorithm addresses the trade-off between convergence speed and steady-state error, though it is an incremental improvement over existing methods.
The paper proposes a hybrid GFANC-FxNLMS algorithm with online clustering to combine fast response and low steady-state error, achieving fast response, very low steady-state error, and high stability with only one pre-trained broadband filter.
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.