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

This paper 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 amplitude- and frequency-modulated 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.

[1]  Hau-Tieng Wu,et al.  The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications , 2011, Signal Process..

[2]  Hau-Tieng Wu,et al.  Synchrosqueezing-Based Recovery of Instantaneous Frequency from Nonuniform Samples , 2010, SIAM J. Math. Anal..

[3]  Bruno Torrésani,et al.  Characterization of signals by the ridges of their wavelet transforms , 1997, IEEE Trans. Signal Process..

[4]  Emmanuel J. Candes,et al.  Detecting highly oscillatory signals by chirplet path pursuit , 2006, gr-qc/0604017.

[5]  I. Daubechies,et al.  Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool , 2011 .

[6]  T. Oberlin,et al.  Theoretical analysis of the second-order synchrosqueezing transform , 2016, Applied and Computational Harmonic Analysis.

[7]  Hau-Tieng Wu,et al.  Heart beat classification from single-lead ECG using the synchrosqueezing transform , 2015, Physiological measurement.

[8]  LJubisa Stankovic,et al.  A measure of some time-frequency distributions concentration , 2001, Signal Process..

[9]  Sylvain Meignen,et al.  Time-Frequency Reassignment and Synchrosqueezing: An Overview , 2013, IEEE Signal Processing Magazine.

[10]  Yi Wang,et al.  ConceFT: concentration of frequency and time via a multitapered synchrosqueezed transform , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[11]  Patrick Flandrin,et al.  Improving the readability of time-frequency and time-scale representations by the reassignment method , 1995, IEEE Trans. Signal Process..

[12]  Les E. Atlas,et al.  Applications of positive time-frequency distributions to speech processing , 1994, IEEE Trans. Speech Audio Process..

[13]  Haizhao Yang.  Statistical analysis of synchrosqueezed transforms , 2014, Applied and Computational Harmonic Analysis.

[14]  Ingrid Daubechies,et al.  A Nonlinear Squeezing of the Continuous Wavelet Transform Based on Auditory Nerve Models , 2017 .

[15]  Sylvain Meignen,et al.  On Demodulation, Ridge Detection, and Synchrosqueezing for Multicomponent Signals , 2017, IEEE Transactions on Signal Processing.

[16]  Sylvain Meignen,et al.  Second-Order Synchrosqueezing Transform or Invertible Reassignment? Towards Ideal Time-Frequency Representations , 2015, IEEE Transactions on Signal Processing.

[17]  K. Kodera,et al.  Analysis of time-varying signals with small BT values , 1978 .

[18]  Sylvain Meignen,et al.  A New Algorithm for Multicomponent Signals Analysis Based on SynchroSqueezing: With an Application to Signal Sampling and Denoising , 2012, IEEE Transactions on Signal Processing.

[19]  The Ligo Scientific Collaboration,et al.  Observation of Gravitational Waves from a Binary Black Hole Merger , 2016, 1602.03837.

[20]  Hau-Tieng Wu,et al.  Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals of Wearable Piezo-Electric Bands. , 2016, IEEE journal of biomedical and health informatics.