Dans cet article, nous présentons le principe de la transformée synchrosqueezée (SST) développée pour améliorer, en utilisant des techniques de réallocation, la qualité des représentations linéaires temps–fréquence, comme les transformées de Fourier à court terme ou en ondelettes. Les transformées réallouées demeurent inversibles, ce qui est fondamental pour l'étude des signaux multicomposantes (MCS), i.e. la superposition de modes modulés à la fois en amplitude et en fréquence, utilisés comme modèle dans de nombreuses applications. Cependant, la SST, dans sa formulation initiale, n'est pas adaptée aux signaux composés de modes fortement modulés, et des améliorations, que nous présentons ici, ont récemment été proposées pour pallier ce défaut. Pour illustrer ces nouveaux développements, nous étudierons le cas des ondes gravitationnelles et des modes à phase oscillante. Dans un second temps, nous montrerons comment utiliser conjointement la SST et la démodulation pour améliorer la reconstruction des modes d'un MCS, et terminerons en évoquant différentes perspectives actuelles autour de la SST.
The general aim of this paper is to introduce the concept of synchrosqueezing transforms (SSTs) that was developed to sharpen linear time–frequency representations (TFRs), like the short-time Fourier or the continuous wavelet transforms, in such a way that the sharpened transforms remain invertible. This property is of paramount importance when one seeks to recover the modes of a multicomponent signal (MCS), corresponding to the superimposition of AM/FM modes, a model often used in many practical situations. After having recalled the basic principles of SST and explained why, when applied to an MCS, it works well only when the modes making up the signal are slightly modulated, we focus on how to circumvent this limitation. We then give illustrations in practical situations either associated with gravitational wave signals or modes with fast oscillating frequencies and discuss how SST can be used in conjunction with a demodulation operator, extending existing results in that matter. Finally, we list a series of different perspectives showing the interest of SST for the signal processing community.
@article{CRPHYS_2019__20_5_449_0, author = {Sylvain Meignen and Thomas Oberlin and Duong-Hung Pham}, title = {Synchrosqueezing transforms: {From} low- to high-frequency modulations and perspectives}, journal = {Comptes Rendus. Physique}, pages = {449--460}, publisher = {Elsevier}, volume = {20}, number = {5}, year = {2019}, doi = {10.1016/j.crhy.2019.07.001}, language = {en}, }
TY - JOUR AU - Sylvain Meignen AU - Thomas Oberlin AU - Duong-Hung Pham TI - Synchrosqueezing transforms: From low- to high-frequency modulations and perspectives JO - Comptes Rendus. Physique PY - 2019 SP - 449 EP - 460 VL - 20 IS - 5 PB - Elsevier DO - 10.1016/j.crhy.2019.07.001 LA - en ID - CRPHYS_2019__20_5_449_0 ER -
Sylvain Meignen; Thomas Oberlin; Duong-Hung Pham. Synchrosqueezing transforms: From low- to high-frequency modulations and perspectives. Comptes Rendus. Physique, Volume 20 (2019) no. 5, pp. 449-460. doi : 10.1016/j.crhy.2019.07.001. https://comptes-rendus.academie-sciences.fr/physique/articles/10.1016/j.crhy.2019.07.001/
[1] A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way, Academic Press, 2008
[2] Decomposition of hardy functions into square integrable wavelets of constant shape, SIAM J. Math. Anal., Volume 15 (1984) no. 4, pp. 723-736
[3] Time-Frequency/Time-Scale Analysis, vol. 10, Academic Press, 1998
[4] Analysis of time-varying signals with small bt values, IEEE Trans. Acoust. Speech Signal Process., Volume 26 (1978), pp. 64-76
[5] Improving the readability of time–frequency and time-scale representations by the reassignment method, IEEE Trans. Signal Process., Volume 43 (1995) no. 5, pp. 1068-1089
[6] The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lond., Ser. A, Math. Phys. Eng. Sci., Volume 454 (1998) no. 1971, pp. 903-995
[7] Empirical mode decomposition as a filter bank, IEEE Signal Process. Lett., Volume 11 (2004) no. 2, pp. 112-114
[8] Empirical wavelet transform, IEEE Trans. Signal Process., Volume 61 (2013) no. 16, pp. 3999-4010
[9] Convex optimization approach to signals with fast varying instantaneous frequency, Appl. Comput. Harmon. Anal., Volume 44 (2018), pp. 89-122
[10] Empirical mode decomposition revisited by multicomponent non-smooth convex optimization, Signal Process., Volume 102 (2014), pp. 313-331
[11] A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models, Wavelets in Medicine and Biology, 1996, pp. 527-546
[12] Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool, Appl. Comput. Harmon. Anal., Volume 30 (2011) no. 2, pp. 243-261
[13] Noise and poise: enhancement of postural complexity in the elderly with a stochastic-resonance-based therapy, Europhys. Lett., Volume 77 (2007)
[14] Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand, Nature, Volume 427 (2004), pp. 344-347
[15] Sleep apnea detection based on thoracic and abdominal movement signals of wearable piezoelectric bands, IEEE J. Biomed. Health Inform., Volume 21 (2017) no. 6, pp. 1533-1545
[16] Heart beat classification from single-lead ecg using the synchrosqueezing transform, Physiol. Meas., Volume 38 (2017) no. 2, pp. 171-187
[17] Synchrosqueezing-based recovery of instantaneous frequency from nonuniform samples, SIAM J. Math. Anal., Volume 43 (2011) no. 5, pp. 2078-2095
[18] Adaptive Analysis of Complex Data Sets, 2011 (PhD thesis, Princeton, NJ, USA)
[19] Time-frequency reassignment and synchrosqueezing: an overview, IEEE Signal Process. Mag., Volume 30 (2013) no. 6, pp. 32-41
[20] The Fourier-based synchrosqueezing transform, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 315-319
[21] Multiresolution monogenic signal analysis using the Riesz–Laplace wavelet transform, IEEE Trans. Image Process., Volume 18 (2009) no. 11, pp. 2402-2418
[22] The monogenic synchrosqueezed wavelet transform: a tool for the decomposition/demodulation of am-fm images, Appl. Comput. Harmon. Anal., Volume 39 (2015), pp. 450-486
[23] Synchrosqueezing-based time–frequency analysis of multivariate data, Signal Process., Volume 106 (2015), pp. 331-341
[24] Conceft: concentration of frequency and time via a multitapered synchrosqueezed transform, Philos. Trans. R. Soc. A, Math. Phys. Eng. Sci., Volume 374 (2016)
[25] Instantaneous frequency and wave shape functions (i), Appl. Comput. Harmon. Anal., Volume 35 (2013), pp. 181-199
[26] Radar Handbook, McGraw-Hill Education, 2008
[27] Applications of positive time–frequency distributions to speech processing, IEEE Trans. Speech Audio Process., Volume 2 (1994) no. 4, pp. 554-566
[28] Detecting highly oscillatory signals by chirplet path pursuit, Appl. Comput. Harmon. Anal., Volume 24 (2008) no. 1, pp. 14-40
[29] et al. Observation of gravitational waves from a binary black hole merger, Phys. Rev. Lett., Volume 116 (2016) no. 6
[30] Analyzing transient-evoked otoacoustic emissions by concentration of frequency and time, J. Acoust. Soc. Am., Volume 144 (2018) no. 1, pp. 448-466
[31] Theoretical analysis of the second-order synchrosqueezing transform, Appl. Comput. Harmon. Anal., Volume 45 (2018), pp. 379-404
[32] On demodulation, ridge detection and synchrosqueezing for multicomponent signals, IEEE Trans. Signal Process., Volume 65 (2017) no. 8, pp. 2093-2103
[33] High-order synchrosqueezing transform for multicomponent signals analysis – with an application to gravitational-wave signal, IEEE Trans. Signal Process., Volume 65 (2017), pp. 3168-3178
[34] Second-order synchrosqueezing transform or invertible reassignment? Towards ideal time–frequency representations, IEEE Trans. Signal Process., Volume 63 (2015), pp. 1335-1344
[35] T. Oberlin, S. Meignen, The second-order wavelet synchrosqueezing transform, in: Proc. 42th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, LA, USA, 5–9 March 2017.
[36] A measure of some time–frequency distributions concentration, Signal Process., Volume 81 (2001) no. 3, pp. 621-631
[37] et al. GW151226: observation of gravitational waves from a 22-solar-mass binary black hole coalescence, Phys. Rev. Lett., Volume 116 (2016) no. 24
[38] Measuring time–frequency information content using the rényi entropies, IEEE Trans. Inf. Theory, Volume 47 (2001) no. 4, pp. 1391-1409
[39] Characterization of signals by the ridges of their wavelet transforms, IEEE Trans. Signal Process., Volume 45 (1997), pp. 2586-2590
[40] Adaptive multimode signal reconstruction from time–frequency representations, Philos. Trans. R. Soc. A, Volume 374 (2016) no. 2065
[41] D. Fourer, G. Peeters, Fast and adaptive blind audio source separation using recursive Levenberg-Marquardt synchrosqueezing, in: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 15–20 April 2018, pp. 766–770.
[42] V. Bruni, M. Tartaglione, D. Vitulano, On the time–frequency reassignment of interfering modes in multicomponent fm signals, in: Proc. 26th European Signal Processing Conference (EUSIPCO), Rome, 3–7 September 2018, pp. 722–726.
[43] Linear and synchrosqueezed time–frequency representations revisited: overview, standards of use, resolution, reconstruction, concentration, and algorithms, Digital Signal Processing, Volume 42 (2015), pp. 1-26
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