# High-Order Synchrosqueezing Transform for Multicomponent Signals Analysis -- With an Application to Gravitational-Wave Signal

arXiv: https://arxiv.org/abs/2004.09399 · HTML: https://mlsanity.com/paper/2004.09399/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [ECG-SleepNet: Deep Learning-Based Comprehensive Sleep Stage Classification Using ECG Signals](https://mlsanity.com/paper/2412.01929/) (2024)
- [Time-Frequency Filtering Meets Graph Clustering](https://mlsanity.com/paper/2510.07503/) (2025)

## Abstract (arXiv)

This study puts forward a generalization of the short-time Fourier-based Synchrosqueezing Transform using a new local estimate of instantaneous frequency. Such a technique enables not only to achieve a highly concentrated time-frequency representation for a wide variety of AM-FM multicomponent signals but also to reconstruct their modes with a high accuracy. Numerical investigation on synthetic and gravitational-wave signals shows the efficiency of this new approach.