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

• URL: http://arxiv.org/abs/2508.13018v1
• Date: Mon, 18 Aug 2025 15:37:11 GMT
• Title: Design and Analysis of Robust Adaptive Filtering with the Hyperbolic Tangent Exponential Kernel M-Estimator Function for Active Noise Control
• Authors: Iam Kim de S. Hermont, Andre R. Flores, Rodrigo C. de Lamare,
• Abstract summary: We develop a filtered-x hyperbolic tangent exponential generalized Kernel M-estimate function (FXHEKM) robust adaptive algorithm.<n>A statistical analysis of the proposed FXHEKM algorithm is carried out along with a study of its computational cost.<n> Numerical results show the efficiency of the proposed FXHEKM algorithm to cancel the presence of the additive spurious signals.
• Score: 19.211198388305807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 \textbf{$\alpha$}-stable noises against competing algorithms.

Related papers

• An Adaptive Re-evaluation Method for Evolution Strategy under Additive Noise [3.92625489118339]
  We propose a novel method to adaptively choose the optimal re-evaluation number for function values corrupted by additive Gaussian white noise.<n>We experimentally compare our method to the state-of-the-art noise-handling methods for CMA-ES on a set of artificial test functions.
  arXiv  Detail & Related papers  (2024-09-25T09:10:21Z)
• Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation [29.114100423416204]
  We propose a blind symbol detection for block-fading linear inter-symbol channels. We design a joint channel estimation and detection scheme that combines the study expectation algorithm and the ubiquitous belief propagation algorithm. We show that the proposed method can learn efficient schedules that generalize well and even outperform coherent BP detection in high signal-to-noise scenarios.
  arXiv  Detail & Related papers  (2024-08-05T08:45:50Z)
• CMA-ES with Adaptive Reevaluation for Multiplicative Noise [1.3108652488669732]
  We develop the reevaluation adaptation CMA-ES (RA-CMA-ES), which computes two update directions using half of the evaluations and adapts the number of reevaluations on the estimated correlation of those two update directions. The numerical simulation shows that the RA-CMA-ES outperforms the comparative method under multiplicative noise.
  arXiv  Detail & Related papers  (2024-05-19T07:42:10Z)
• Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples [58.68233326265417]
  Non-image data are prevalent in real applications and tend to be noisy. Risk-sensitive SDE is a type of differential equation (SDE) parameterized by the risk vector. We conduct systematic studies for both Gaussian and non-Gaussian noise distributions.
  arXiv  Detail & Related papers  (2024-02-03T08:41:51Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods [3.8286082196845466]
  Correntropy-based descent gradient (CGD) and correntropy-based decoupled orientation estimation (CDOE) Traditional methods rely on the mean squared error (MSE) criterion, making them vulnerable to external acceleration and magnetic interference. New algorithms demonstrate significantly lower computational complexity than Kalman filter-based approaches.
  arXiv  Detail & Related papers  (2023-04-13T13:57:33Z)
• Optimal Algorithms for the Inhomogeneous Spiked Wigner Model [89.1371983413931]
  We derive an approximate message-passing algorithm (AMP) for the inhomogeneous problem. We identify in particular the existence of a statistical-to-computational gap where known algorithms require a signal-to-noise ratio bigger than the information-theoretic threshold to perform better than random.
  arXiv  Detail & Related papers  (2023-02-13T19:57:17Z)
• Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate [35.60818658948953]
  We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey's biweight M-estimate CG (TbMCG) In particular, the TbMCG algorithm can achieve a faster convergence while retaining a reduced computational complexity. Simulation results confirm the excellent performance of the proposed TbMCG algorithm for system identification and active noise control applications.
  arXiv  Detail & Related papers  (2022-03-19T01:02:43Z)
• Differentiable Annealed Importance Sampling and the Perils of Gradient Noise [68.44523807580438]
  Annealed importance sampling (AIS) and related algorithms are highly effective tools for marginal likelihood estimation. Differentiability is a desirable property as it would admit the possibility of optimizing marginal likelihood as an objective. We propose a differentiable algorithm by abandoning Metropolis-Hastings steps, which further unlocks mini-batch computation.
  arXiv  Detail & Related papers  (2021-07-21T17:10:14Z)
• Momentum Accelerates the Convergence of Stochastic AUPRC Maximization [80.8226518642952]
  We study optimization of areas under precision-recall curves (AUPRC), which is widely used for imbalanced tasks. We develop novel momentum methods with a better iteration of $O (1/epsilon4)$ for finding an $epsilon$stationary solution. We also design a novel family of adaptive methods with the same complexity of $O (1/epsilon4)$, which enjoy faster convergence in practice.
  arXiv  Detail & Related papers  (2021-07-02T16:21:52Z)
• Plug-And-Play Learned Gaussian-mixture Approximate Message Passing [71.74028918819046]
  We propose a plug-and-play compressed sensing (CS) recovery algorithm suitable for any i.i.d. source prior. Our algorithm builds upon Borgerding's learned AMP (LAMP), yet significantly improves it by adopting a universal denoising function within the algorithm. Numerical evaluation shows that the L-GM-AMP algorithm achieves state-of-the-art performance without any knowledge of the source prior.
  arXiv  Detail & Related papers  (2020-11-18T16:40:45Z)
• Stochastic Hard Thresholding Algorithms for AUC Maximization [49.00683387735522]
  We develop a hard thresholding algorithm for AUC in distributiond classification. We conduct experiments to show the efficiency and effectiveness of the proposed algorithms.
  arXiv  Detail & Related papers  (2020-11-04T16:49:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.