Local Adaptive Time-Frequency Bidirectional Synchrosqueezing Transform

Extracting time-frequency (TF) features is an essential way to describe the characteristics of non-stationary signals. The existing synchrosqueezing methods post-squeeze the TF representation in a single direction to preserve the reconstruction ability, thus they are only effective for single-direction weakly modulated components. To go beyond this limitation, we introduce a new criterion based on the concentration to locally category the components into harmonic-like and impulsive-like parts. Thereby simultaneously squeezing the TF plane in two directions and ensuring the recovery of original signals. We validate and compare the proposed approach with existing methods in both artificial and real-world signals.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10888318
Primary Topic
Advanced Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Local Adaptive Time-Frequency Bidirectional Synchrosqueezing Transform

Yuhe Liao, Cuiwentong Xu
Advanced Algorithms and Applications
article

Local Adaptive Time-Frequency Bidirectional Synchrosqueezing Transform

Yuhe Liao, Cuiwentong Xu
article en

Abstract

Extracting time-frequency (TF) features is an essential way to describe the characteristics of non-stationary signals. The existing synchrosqueezing methods post-squeeze the TF representation in a single direction to preserve the reconstruction ability, thus they are only effective for single-direction weakly modulated components. To go beyond this limitation, we introduce a new criterion based on the concentration to locally category the components into harmonic-like and impulsive-like parts. Thereby simultaneously squeezing the TF plane in two directions and ensuring the recovery of original signals. We validate and compare the proposed approach with existing methods in both artificial and real-world signals.

Xi'an Jiaotong University (CN)
Ministry of Education, National Natural Science Foundation of China, National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 95%
Advanced Algorithms and Applications
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Local Adaptive Time-Frequency Bidirectional Synchrosqueezing Transform — Yuhe Liao, Cuiwentong Xu · (2025) | TGRS Research Map | TGRS