Time-Frequency Reassignment and Synchrosqueezing: An Overview

Auger, François; Flandrin, Patrick; Lin, Yuting; McLaughlin, Stephen; Meignen, Sylvain; Oberlin, Thomas; Wu, Hau‐Tieng

doi:10.1109/msp.2013.2265316

Cited by 512 publications

(300 citation statements)

References 27 publications

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“…These techniques sharpen the TF representation by taking the phase information hidden inside the linear TF analysis into account. For details, we refer the reader with interest to [38,17] for the review material. To demonstrate this reflection effect, we start from a harmonic function f 1 (t) = cos(2π2.5t) and sample it with the ISR ψ 1 (t) = 6 + (t−80/π) 2 800…”

Section: Reflection Effect and Time-frequency Analysismentioning

confidence: 99%

When Interpolation-Induced Reflection Artifact Meets Time–Frequency Analysis

Lin¹,

Flandrin

2016

IEEE Trans. Biomed. Eng.

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ABSTRACT. While extracting the temporal dynamical features based on the time-frequency analyses, like the reassignment and synchrosqueezing transform, attracts more and more interest in bio-medical data analysis, we should be careful about artifacts generated by interpolation schemes, in particular when the sampling rate is not significantly higher than the frequency of the oscillatory component we are interested in. In this study, we formulate the problem called the reflection effect and provide a theoretical justification of the statement. We also show examples in the anesthetic depth analysis with clear but undesirable artifacts. The results show that the artifact associated with the reflection effect exists not only theoretically but practically. Its influence is pronounced when we apply the time-frequency analyses to extract the time-varying dynamics hidden inside the signal. In conclusion, we have to carefully deal with the artifact associated with the reflection effect by choosing a proper interpolation scheme.

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Section: Reflection Effect and Time-frequency Analysismentioning

confidence: 99%

When Interpolation-Induced Reflection Artifact Meets Time–Frequency Analysis

Lin¹,

Flandrin

2016

IEEE Trans. Biomed. Eng.

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“…Synchrosqueezing transform (SST) is a relatively new technique based on the combination of time-frequency methods followed by a reassignment step [15,16] that can be also regarded as an alternative to the EMD method considering their similar time-frequency resolution. SST can improve the energy concentration of time-frequency representation by applying a post-processing reallocation to the original representation.…”

Section: Introductionmentioning

confidence: 99%

The Synchrosqueezing Algorithm Based on Generalized S-transform for High-Precision Time-Frequency Analysis

Chen

et al. 2017

Applied Sciences

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Abstract:In this paper, a new time-frequency analysis method-Synchrosqueezing Generalized S-transform (SSGST)-is proposed to meet the needs of high-resolution seismic signal processing and interpretation. The basic wavelet of the generalized S-transform (GST) in the paper is a modulated harmonic wave with four undetermined parameters that can be constructed by adjusting the four parameters to make the GST more suitable for seismic signals processing. The SSGST method squeezes and reconstructs the complex coefficient spectra of GST results along the frequency direction so that the energy distributions on the time-frequency spectra are concentrated around the real instantaneous frequency of the signal; thus, the time-frequency resolution can be improved. Based on mathematical theory, the basic principle of the new transformation method is given, and the mathematical expressions of the positive transformation and lossless inverse transformation of the method are strictly deduced. The experimental results of numerical signals illustrate that the proposed method can correctly decompose signals with different spectral characteristics into a high time-frequency resolution spectrum and can recovery the original signal from the time-frequency spectrum with satisfying reconstructing accuracy. Application on field seismic data shows the superiority of the new method in seismic time-frequency analysis for hydrocarbon detection.

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“…Longer windows are more suitable for resolving signal components in the frequency domain, while short windows are more suitable for resolving signal components in time. One way to avoid this problem is to shift the power to the center of gravity by using the method of reassignment [23]. The reassignment method achieves a high time-frequency (TF) resolution, but it cannot be used to reconstruct the original signals.…”

Section: Introductionmentioning

confidence: 99%

“…The reassignment method achieves a high time-frequency (TF) resolution, but it cannot be used to reconstruct the original signals. The synchrosqueezing transformation estimates the local center of gravity only along the frequency axis [23]. The methods of reassignment and synchrosqueezing have previously been applied to HRV estimation [24,25].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Time-Frequency Distributions for Estimation of Instantaneous Frequency of Heart Rate Variability Signals

2017

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Abstract:The instantaneous frequency (IF) of a non-stationary signal is usually estimated from a time-frequency distribution (TFD). The IF of heart rate variability (HRV) is an important parameter because the power in a frequency band around the IF can be used for the interpretation and analysis of the respiratory rate but also for a more accurate analysis of heart rate (HR) signals. In this study, we compare the performance of five states of the art kernel-based time-frequency distributions (TFDs) in terms of their ability to accurately estimate the IF of HR signals. The selected TFDs include three widely used fixed kernel methods: the modified B distribution, the S-method and the spectrogram; and two adaptive kernel methods: the adaptive optimal kernel TFD and the recently developed adaptive directional TFD. The IF of the respiratory signal, which is usually easier to estimate as the respiratory signal is a mono-component with small amplitude variations with time, is used as a reference to examine the accuracy of the HRV IF estimates. Experimental results indicate that the most reliable estimates are obtained using the adaptive directional TFD in comparison to other commonly used methods such as the adaptive optimal kernel TFD and the modified B distribution.

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Time-Frequency Reassignment and Synchrosqueezing: An Overview

Cited by 512 publications

References 27 publications

When Interpolation-Induced Reflection Artifact Meets Time–Frequency Analysis

When Interpolation-Induced Reflection Artifact Meets Time–Frequency Analysis

The Synchrosqueezing Algorithm Based on Generalized S-transform for High-Precision Time-Frequency Analysis

Performance Comparison of Time-Frequency Distributions for Estimation of Instantaneous Frequency of Heart Rate Variability Signals

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