A Novel Ridge Detector for Nonstationary Multicomponent Signals: Development and Application to Robust Mode Retrieval

TL;DR

This paper introduces a new ridge detection method for nonstationary multicomponent signals that enhances robustness against noise, improving mode retrieval in time-frequency analysis.

Contribution

It develops a novel ridge detection and mode retrieval approach based on short-time Fourier transform analysis, outperforming existing methods in noisy conditions.

Findings

Enhanced noise robustness in ridge detection

Improved accuracy in mode retrieval

Superior performance compared to state-of-the-art methods

Abstract

Time-frequency analysis is often used to study non stationary multicomponent signals, which can be viewed as the surperimposition of modes, associated with ridges in the TF plane. To understand such signals, it is essential to identify their constituent modes. This is often done by performing ridge detection in the time-frequency plane which is then followed by mode retrieval. Unfortunately, existing ridge detectors are often not enough robust to noise therefore hampering mode retrieval. In this paper, we therefore develop a novel approach to ridge detection and mode retrieval based on the analysis of the short-time Fourier transform of multicomponent signals in the presence of noise, which will prove to be much more robust than state-of-the-art methods based on the same time-frequency representation.