Adaptive Cohen's Class Time-Frequency Distribution

TL;DR

This paper introduces a novel adaptive kernel-based Cohen's class time-frequency distribution method that automatically adjusts to input signals for improved noise suppression in the Wigner-Ville domain.

Contribution

It develops a least-squares adaptive filter approach integrating Wiener filtering principles into CCTFD for automatic kernel adjustment and enhanced denoising performance.

Findings

Outperforms existing methods in noise suppression tasks.

Automatically adjusts kernel functions based on input signals.

Demonstrates superior denoising in Wigner-Ville distribution domain.

Abstract

Inspired by the use of adaptive kernel-based Cohen's class time-frequency distributions (CCTFDs) for cross-term suppression, this paper aims to explore novel adaptive kernel functions for denoising. We integrate Wiener filter principle and the time-frequency filtering mechanism of CCTFD to design the least-squares adaptive filter method in the Wigner-Ville distribution (WVD) domain, giving birth to the least-squares adaptive filter-based CCTFD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error denoising in the WVD domain. Some examples are also carried out to demonstrate that the proposed adaptive CCTFD outperforms some state-of-the-arts in noise suppression.