Design and Analysis of Robust Adaptive Filtering with the Hyperbolic Tangent Exponential Kernel M-Estimator Function for Active Noise Control

2025/08/18 by Iam Kim de S. Hermont, Hermont, Iam Kim de S., André R. Flores +3
Engineering · #Advanced Adaptive Filtering Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2508.13018

openalex publication_date 2025/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this work, we propose a robust adaptive filtering approach for active noise control applications in the presence of impulsive noise. In particular, we develop the filtered-x hyperbolic tangent exponential generalized Kernel M-estimate function (FXHEKM) robust adaptive algorithm. A statistical analysis of the proposed FXHEKM algorithm is carried out along with a study of its computational cost. In order to evaluate the proposed FXHEKM algorithm, the mean-square error (MSE) and the average noise reduction (ANR) performance metrics have been adopted. Numerical results show the efficiency of the proposed FXHEKM algorithm to cancel the presence of the additive spurious signals, such as α-stable noises against competing algorithms.

Citations

• A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control Based on Deep Learning
• Reduced-Rank Adaptive Filtering Based on Joint Iterative Optimization of Adaptive Filters
• Active noise control: a tutorial review
• Adaptive noise cancelling: Principles and applications
• A Secondary Path-Decoupled Active Noise Control Algorithm Based on Deep Learning

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• Study of General Robust Subband Adaptive Filtering
• Proportionate Adaptive Filtering under Correntropy Criterion in Impulsive Noise Environments
• Sparsity-Aware Filtered-X Affine Projection Algorithms for Active Noise Control
• Robust Adaptive Filtering Based on Exponential Functional Link Network
• On Error Saturation Nonlinearities for LMS Adaptation in Impulsive Noise