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

2026-01-22

AI Summary

This work proposes a hybrid active noise control architecture that synergistically combines the Generalized Filtered-x Affine Projection Algorithm (GFANC) and the Filtered-x Normalized Least Mean Squares (FxNLMS) algorithm to overcome their respective limitations. While conventional FxNLMS suffers from slow convergence and potential instability, GFANC offers rapid response but exhibits large steady-state error and limited adaptability. The proposed method leverages GFANC at the frame level to generate an initial filter estimate, which is then refined by FxNLMS operating at the sample rate for precise adaptation. An online clustering module is incorporated to suppress unnecessary reinitializations, enhancing stability. Requiring only a single pre-trained broadband filter, the approach achieves fast transient response, extremely low steady-state error, and high robustness, thereby significantly improving the overall performance of active noise control systems.

Technology Category

• Machine Learning: Active Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Active Noise Control
• FxNLMS
• GFANC
• Convergence
• Stability

Innovation

• Hybrid Active Noise Control
• Online Clustering
• GFANC
• FxNLMS
• Stability Enhancement