S. Meignen and D. Pham, On multicomponent signal denoising and mode reconstruction from short-time fourier transform, IEEE Transactions on Signal Processing, 2018.

D. H. Pham, S. Meignen, N. Dia, J. Fontecave-jallon, and B. Rivet, Phonocardiogram signal denoising based on non-negative matrix factorization and adaptive contour representation computation, IEEE Signal Processing Letters, vol.25, pp.1475-1479, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01855855

D. Pham and S. Meignen, Second-order synchrosqueezing transform: The wavelet case, comparisons and applications, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01586372

D. Pham and S. Meignen, An adaptive computation of contour representations for mode decomposition, IEEE Signal Processing Letters, vol.24, pp.1596-1600, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01586387

D. Pham and S. Meignen, High-order synchrosqueezing transform for multicomponent signals analysis-with an application to gravitational-wave signal, IEEE Transactions on Signal Processing, vol.65, pp.3168-3178, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01912852

S. Meignen, D. Pham, and S. Mclaughlin, On demodulation, ridge detection and synchrosqueezing for multicomponent signals, IEEE Transactions on Signal Processing, vol.65, issue.8, pp.2093-2103, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01586414

D. Pham and S. Meignen, A novel thresholding technique for the denoising of multicomponent signals, IEEE ICASSP, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01912884

D. Pham, Poster for signal processing meets deep learning, IEEE-EURASIP Summer School, 2017.

D. Pham, Analysis, synthesis and transformations by reassignment, emd and synchrosqueezing, Workshop ASTRES, 2016.

F. Auger, P. Flandrin, P. Gonçalvès, and O. Lemoine, The Time-Frequency Toolbox (TFTB)-For Use with MATLAB, 1996.

J. R. Mitchell and J. Wang, Expanding application of the wiggers diagram to teach cardiovascular physiology, Advances in physiology education, vol.38, pp.170-175, 2014.

L. Cohen, Time-frequency Analysis: Theory and Applications, 1995.

B. Boashash, Time frequency signal analysis and processing-A comprehensive reference, 2003.

S. V. Vaseghi, Advanced digital signal processing and noise reduction, 2008.

E. Sejdi?, I. Djurovi?, and J. Jiang, Time-frequency feature representation using energy concentration: An overview of recent advances, Digital Signal Processing, vol.19, issue.1, pp.153-183, 2009.

A. Haar, Zur theorie der orthogonalen funktionensysteme, Mathematische Annalen, vol.69, issue.3, pp.331-371, 1910.
URL : https://hal.archives-ouvertes.fr/hal-01333722

D. Gabor, Electrical Engineers-Part III: Radio and Communication Engineering, Journal of the Institution, vol.93, pp.429-441, 1946.

W. W. Au and M. C. Hastings, Principles of marine bioacoustics, 2008.

A. W. Rihaczek, Principles of high-resolution radar, artech house radar library, 1996.

E. J. Candes, P. R. Charlton, and H. Helgason, Detecting highly oscillatory signals by chirplet path pursuit, Applied and Computational Harmonic Analysis, vol.24, issue.1, pp.14-40, 2008.

B. P. Abbott, Observation of gravitational waves from a binary black hole merger, Phys. Rev. Lett, vol.116, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01302381

N. Dia, J. Fontecave-jallon, P. Guméry, and B. Rivet, Application de la factorisation non-négative des matrices (nmf) pour le débruitage des signaux phonocardiographiques, XXVIeme Colloque GRETSI à Juan, 2017.

Y. Y. Lin, H. Wu, C. A. Hsu, P. C. Huang, Y. H. Huang et al., Sleep apnea detection based on thoracic and abdominal movement signals of wearable piezo-electric bands, IEEE Journal of Biomedical and Health Informatics, 2016.

C. L. Herry, M. Frasch, A. J. Seely, and H. Wu, Heart beat classification from singlelead ecg using the synchrosqueezing transform, Physiological Measurement, vol.38, issue.2, pp.171-187, 2017.

P. Flandrin, Time-frequency/time-scale analysis, vol.10, 1998.

L. Cohen and C. Lee, Instantaneous frequency, its standard deviation and multicomponent signals, Advanced Algorithms and Architectures for Signal Processing III, vol.975, pp.186-209, 1988.

L. Cohen, What is a multicomponent signal?, Acoustics, Speech, and Signal Processing, 1992. ICASSP-92, vol.5, pp.113-116, 1992.

D. Dimitriadis, P. Maragos, and A. Potamianos, Robust am-fm features for speech recognition, IEEE signal processing letters, vol.12, issue.9, pp.621-624, 2005.

V. Murray, C. Agurto, S. Barriga, M. S. Pattichis, and P. Soliz, Real-time diabetic retinopathy patient screening using multiscale am-fm methods, 19th IEEE International Conference on, pp.525-528, 2012.

S. Washimkar and S. Chede, Application of fcm clustering on am-fm to detect mri disease progression for multiple sclerosis, Computational Intelligence and Communication Networks (CICN), 2015 International Conference on, pp.283-287, 2015.

S. Taran, V. Bajaj, and D. Sharma, Teo separated am-fm components for identification of apnea eeg signals, 2017 IEEE 2nd International Conference on, pp.391-395, 2017.

J. P. Havlicek, D. S. Harding, and A. C. Bovik, The multicomponent am-fm image representation, IEEE Transactions on Image Processing, vol.5, issue.6, pp.1094-1100, 1996.

M. S. Pattichis, G. Panayi, A. C. Bovik, and S. Hsu, Fingerprint classification using an am-fm model, IEEE Transactions on Image Processing, vol.10, issue.6, pp.951-954, 2001.

K. Kodera, C. D. Villedary, and R. Gendrin, A new method for the numerical analysis of non-stationary signals, Physics of the Earth and Planetary Interiors, vol.12, issue.23, pp.142-150, 1976.

K. Kodera, R. Gendrin, and C. Villedary, Analysis of time-varying signals with small bt values, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.26, pp.64-76, 1978.

F. Auger and P. Flandrin, Improving the readability of time-frequency and time-scale representations by the reassignment method, IEEE Transactions on Signal Processing, vol.43, issue.5, pp.1068-1089, 1995.

I. Daubechies and S. Maes, A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models, Wavelets in medicine and biology, pp.527-546, 1996.

I. Daubechies, J. Lu, and H. Wu, Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool, Applied and Computational Harmonic Analysis, vol.30, issue.2, pp.243-261, 2011.

G. Thakur and H. Wu, Synchrosqueezing-based recovery of instantaneous frequency from nonuniform samples, SIAM Journal on Mathematical Analysis, vol.43, pp.2078-2095, 2011.

G. Thakur, E. Brevdo, N. S. Fu?kar, and H. Wu, The synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications, Signal Process, vol.93, pp.1079-1094, 2013.

M. Clausel, T. Oberlin, and V. Perrier, The monogenic synchrosqueezed wavelet transform: a tool for the decomposition/demodulation of am-fm images, Applied and Computational Harmonic Analysis, vol.39, pp.450-486, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00875037

H. Yang and L. Ying, Synchrosqueezed wave packet transform for 2D mode decomposition, SIAM Journal on Imaging Sciences, vol.6, pp.1979-2009, 2013.

H. Yang, Synchrosqueezed wave packet transforms and diffeomorphism based spectral analysis for 1d general mode decompositions, Applied and Computational Harmonic Analysis, vol.39, pp.33-66, 2015.

I. Daubechies, Y. Wang, and H. Wu, Conceft: concentration of frequency and time via a multitapered synchrosqueezed transform, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.374, 2016.

M. Skolnik, Radar Handbook. McGraw-Hill Education, 2008.

J. W. Pitton, L. E. Atlas, and P. J. Loughlin, Applications of positive time-frequency distributions to speech processing, IEEE Transactions on Speech and Audio Processing, vol.2, issue.4, pp.554-566, 1994.

T. Oberlin, S. Meignen, and V. Perrier, The fourier-based synchrosqueezing transform, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.315-319, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00916088

T. Oberlin, S. Meignen, and V. Perrier, Second-order synchrosqueezing transform or invertible reassignment? Towards ideal time-frequency representations, IEEE Transactions on Signal Processing, vol.63, pp.1335-1344, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00994883

R. Behera, S. Meignen, and T. Oberlin, Theoretical analysis of the second-order synchrosqueezing transform, Applied and Computational Harmonic Analysis, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01220017

C. Li and M. Liang, A generalized synchrosqueezing transform for enhancing signal time-frequency representation, Signal Processing, vol.92, pp.2264-2274, 2012.

S. Wang, X. Chen, G. Cai, B. Chen, X. Li et al., Matching demodulation transform and synchrosqueezing transform in time-frequency analysis, IEEE Trans. Signal Process, vol.62, issue.1, pp.69-84, 2014.

D. Groutage, J. Schempp, and L. Cohen, Characterization and analysis of marine mammal sounds using time-frequency and time-prony techniques, Proceedings, vol.1, p.253, 1994.

X. Rodet, Time-domain formant-wave-function synthesis, Spoken Language Generation and Understanding, pp.429-441, 1980.

T. F. Quatieri and R. J. Mcaulay, Audio signal processing based on sinusoidal analysis/synthesis, Applications of digital signal processing to audio and acoustics, pp.343-416, 2002.

N. H. Fletcher and T. D. Rossing, The physics of musical instruments, 2012.

S. L. Garrett, J. A. Smith, R. W. Smith, B. J. Heidrich, and M. D. Heibel, Fissionpowered in-core thermoacoustic sensor, Applied Physics Letters, vol.108, issue.14, p.144102, 2016.

Y. Lim, B. Shinn-cunningham, and T. Gardner, Sparse contour representations of sound, IEEE Signal Processing Letters, vol.19, pp.684-687, 2012.

S. Meignen, T. Gardner, and T. Oberlin, Time-frequency ridge analysis based on reassignment vector, Proceedings of the 23st European Signal Processing Conference (EUSIPCO-15), 2015.
URL : https://hal.archives-ouvertes.fr/hal-01327104

S. Meignen, T. Oberlin, P. Depalle, P. Flandrin, and S. Mclaughlin, Adaptive multimode signal reconstruction from time-frequency representations, Phil. Trans. R. Soc. A, vol.374, p.20150205, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01912880

H. Fan, Y. Jiang, and G. Kuang, Target detection of non-stationary radar signal and riemannian geometry, Geometric Science of Information, pp.821-828, 2013.

J. G. Proakis, Digital signal processing: principles algorithms and applications, 2001.

A. Vretblad, Fourier analysis and its applications, vol.223, 2006.

M. Costa, A. A. Priplata, L. A. Lipsitz, Z. Wu, N. E. Huang et al., Noise and poise: Enhancement of postural complexity in the elderly with a stochastic-resonance-based therapy, Europhysics Letters (EPL), vol.77, p.68008, 2007.

D. A. Cummings, R. A. Irizarry, N. E. Huang, T. P. Endy, A. Nisalak et al., Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand, Nature, vol.427, pp.344-347, 2004.

N. E. Huang and Z. Wu, A review on Hilbert-huang transform: Method and its applications to geophysical studies, Reviews of Geophysics, vol.46, 2008.

F. Auger, P. Flandrin, Y. Lin, S. Mclaughlin, S. Meignen et al., Time-frequency reassignment and synchrosqueezing: An overview, IEEE Signal Processing Magazine, vol.30, issue.6, pp.32-41, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00983755

F. Hlawatsch and F. Auger, Time-Frequency Analysis: Concepts and Methods, 2008.

B. Boashash, Estimating and interpreting the instantaneous frequency of a signal. i. fundamentals, Proceedings of the IEEE, vol.80, issue.4, pp.520-538, 1992.

E. Bedrosian, A product theorem for hilbert transforms, Proceedings of the IEEE, vol.51, issue.5, pp.868-869, 1963.

G. Xu, X. Wang, X. Xu, L. Zhou, and L. Shao, Time-varying bandpass filter based on assisted signals for am-fm signal separation: A revisit, Journal of Signal and Information Processing, vol.4, issue.03, p.229, 2013.

G. F. Hlawatsch and . Boudreaux-bartels, Linear and quadratic time-frequency signal representations, IEEE M SP, vol.9, issue.2, pp.21-67, 1992.

S. W. Smith, The scientist and engineer's guide to digital signal processing, 1997.

S. Mallat, A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way, 2008.

T. Oberlin, Analyse de signaux multicomposantes : contributions à la décomposition modale Empirique, aux représentations temps-fréquence et au Synchrosqueezing, 2013.

I. Daubechies, Ten Lectures on Wavelets, 1992.

S. Meignen, T. Oberlin, and S. Mclaughlin, Multicomponent signal denoising with synchrosqueezing, Statistical Signal Processing Workshop (SSP), pp.660-663, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00872534

J. O. Smith, Spectral audio signal processing. W3K publishing, 2011.

B. Yang, A study of inverse short-time fourier transform, Acoustics, Speech and Signal Processing, pp.3541-3544, 2008.

H. Nawab and T. Quatieri, In Chapter 6 of Advanced Topics in Signal Processing, pp.289-337, 1988.

F. Auger, E. Chassande-mottin, and P. Flandrin, Making reassignment adjustable: The levenberg-marquardt approach, Acoustics, Speech and Signal Processing, pp.3889-3892, 2012.
URL : https://hal.archives-ouvertes.fr/ensl-00654808

D. Fourer, F. Auger, and P. Flandrin, Recursive versions of the levenberg-marquardt reassigned spectrogram and of the synchrosqueezed stft, Acoustics, Speech and Signal Processing, pp.4880-4884, 2016.

S. Meignen, T. Oberlin, and S. Mclaughlin, A new algorithm for multicomponent signals analysis based on synchrosqueezing: With an application to signal sampling and denoising, IEEE Transactions on Signal Processing, vol.60, issue.11, pp.5787-5798, 2012.
DOI : 10.1109/tsp.2012.2212891

R. Carmona, W. Hwang, and B. Torresani, Characterization of signals by the ridges of their wavelet transforms, IEEE Transactions on Signal Processing, vol.45, pp.2586-2590, 1997.
URL : https://hal.archives-ouvertes.fr/hal-01223134

C. K. Chui, Y. Lin, and H. Wu, Real-time dynamics acquisition from irregular samples-with application to anesthesia evaluation, Analysis and Applications, vol.14, pp.537-590, 2016.
DOI : 10.1142/s0219530515500165
URL : http://arxiv.org/pdf/1406.1276

H. Wu, Adaptive analysis of complex data sets, 2012.

D. Fourer, F. Auger, K. Czarnecki, S. Meignen, and P. Flandrin, Chirp rate and instantaneous frequency estimation: application to recursive vertical synchrosqueezing, IEEE Signal Processing Letters, issue.99, 2017.
DOI : 10.1109/lsp.2017.2714578
URL : https://hal.archives-ouvertes.fr/hal-01579909

T. Oberlin and S. Meignen, The second-order wavelet synchrosqueezing transform, 42th International Conference on Acoustics, Speech, and Signal Processing, 2017.
DOI : 10.1109/icassp.2017.7952906
URL : https://hal.archives-ouvertes.fr/hal-01913971

R. G. Baraniuk, P. Flandrin, A. J. Janssen, and O. J. Michel, Measuring time-frequency information content using the rényi entropies, IEEE Transactions on Information Theory, vol.47, issue.4, pp.1391-1409, 2001.
DOI : 10.1109/18.923723
URL : https://pure.tue.nl/ws/files/3235529/Metis235363.pdf

L. Stankovi?, A measure of some time-frequency distributions concentration, Signal Processing, vol.81, issue.3, pp.621-631, 2001.

D. Donoho and I. Johnstone, Ideal spatial adaptation via wavelet shrinkage, Biometrika, vol.81, pp.425-455, 1994.
DOI : 10.2307/2337118
URL : http://www-stat.stanford.edu/~imj/WEBLIST/1994/isaws.pdf

L. Sachs, Applied statistics: a handbook of techniques, 2012.

C. Villani, Topics in optimal transportation, Stud. Math. AMS, Providence, vol.58, 2003.
DOI : 10.1090/gsm/058

L. Stankovic, M. Dakovic, and V. Ivanovic, Performance of spectrogram as if estimator, Electronics Letters, vol.37, issue.12, pp.797-799, 2001.

B. Barkat and B. Boashash, A high-resolution quadratic time-frequency distribution for multicomponent signals analysis, IEEE Transactions on Signal Processing, vol.49, issue.10, pp.2232-2239, 2001.
DOI : 10.1109/78.950779

M. Z. Ikram and G. T. Zhou, Estimation of multicomponent polynomial phase signals of mixed orders, Signal Processing, vol.81, issue.11, pp.2293-2308, 2001.
DOI : 10.1016/s0165-1684(01)00095-0

S. Olhede and A. Walden, A generalized demodulation approach to time-frequency projections for multicomponent signals, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol.461, pp.2159-2179, 2005.
DOI : 10.1098/rspa.2005.1455
URL : http://spiral.imperial.ac.uk/bitstream/10044/1/1157/1/WavDemodulateRed.pdf

Z. Yu, Y. Sun, and W. Jin, A novel generalized demodulation approach for multicomponent signals, Signal Processing, vol.118, pp.188-202, 2016.
DOI : 10.1016/j.sigpro.2015.07.001

D. Griffin and J. Lim, Signal estimation from modified short-time fourier transform, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.32, issue.2, pp.236-243, 1984.
DOI : 10.1109/icassp.1983.1172092
URL : http://hil.t.u-tokyo.ac.jp/~kameoka/SAP/papers/Griffin1984__Signal_Estimation_from_Modified_Short-Time_Fourier_Transform.pdf

J. Allen, Short term spectral analysis, synthesis, and modification by discrete fourier transform, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.25, issue.3, pp.235-238, 1977.
DOI : 10.1109/tassp.1977.1162950

N. Delprat, B. Escudie, P. Guillemain, R. Kronland, P. Tchamitchian et al., Asymptotic wavelet and Gabor analysis: Extraction of instantaneous frequencies, Information Theory, IEEE Transactions on, vol.38, pp.644-664, 1992.
DOI : 10.1109/18.119728
URL : https://hal.archives-ouvertes.fr/hal-01222729

D. E. Newland, Ridge and phase identification in the frequency analysis of transient signals by harmonic wavelets, Journal of Vibration and Acoustics, vol.121, issue.2, pp.149-155, 1999.

S. Meignen, T. Oberlin, and S. Mclaughlin, Fully adaptive mode decomposition from time-frequency ridges, Acoustics, Speech and Signal Processing, pp.3884-3888, 2017.
DOI : 10.1109/icassp.2017.7952884
URL : https://hal.archives-ouvertes.fr/hal-01913968

W. H. Organization, Global status report on noncommunicable diseases 2017, 2017.

H. Koymen, B. Altay, and Y. Z. Ider, A study of prosthetic heart valve sounds, IEEE transactions on biomedical engineering, issue.11, pp.853-863, 1987.
DOI : 10.1109/tbme.1987.326006

J. Singh and R. Anand, Computer aided analysis of phonocardiogram, Journal of Medical Engineering & Technology, vol.31, pp.319-323, 2007.
DOI : 10.1080/03091900500282772

D. Gradolewski and G. Redlarski, Wavelet-based denoising method for real phonocardiography signal recorded by mobile devices in noisy environment, Computers in Biology and Medicine, vol.52, pp.119-129, 2014.
DOI : 10.1016/j.compbiomed.2014.06.011

S. Messer, J. Agzarian, and D. Abbott, Optimal wavelet denoising for phonocardiograms, Microelectronics Journal, vol.32, pp.931-941, 2001.
DOI : 10.1016/s0026-2692(01)00095-7

P. Varady, Wavelet-based adaptive denoising of phonocardiographic records, Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001.
DOI : 10.1109/iembs.2001.1020582

A. Kumar and G. Saha, A discrete wavelet packet transform based approach for selection of artifact-free phonocardiogram segment, Emerging Applications of Information Technology (EAIT), pp.83-88, 2014.
DOI : 10.1109/eait.2014.52

O. Beya, B. Jalil, E. Fauvet, and O. Laligant, Empirical modal decomposition applied to cardiac signals analysis, Wavelet Applications in Industrial Processing VII, 2010.
DOI : 10.1117/12.840667

A. Gavrovska, M. Slavkovic, I. Reljin, and B. Reljin, Application of wavelet and emd-based denoising to phonocardiograms, Signals, Circuits and Systems (ISSCS), 2013 International Symposium on, pp.1-4, 2013.
DOI : 10.1109/isscs.2013.6651264

O. Beya, E. Fauvet, and O. Laligant, Eda, approche non linéaire de débruitage des signaux cardiaques, CORESA, 2013.

D. Lahat, T. Adali, and C. Jutten, Multimodal data fusion: An overview of methods, challenges, and prospects, Proceedings of the IEEE, vol.103, pp.1449-1477, 2015.
DOI : 10.1109/jproc.2015.2460697
URL : https://hal.archives-ouvertes.fr/hal-01179853

F. Sedighin, M. Babaie-zadeh, B. Rivet, and C. Jutten, Multimodal soft nonnegative matrix co-factorization for convolutive source separation, IEEE Transactions on Signal Processing, vol.65, pp.3179-3190, 2017.
DOI : 10.1109/tsp.2017.2679692
URL : https://hal.archives-ouvertes.fr/hal-01514878

B. Rivet, M. Duda, A. Guerin-dugue, C. Jutten, and P. Comon, Multimodal approach to estimate the ocular movements during EEG recordings: A coupled tensor factorization method, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015.
DOI : 10.1109/embc.2015.7319999
URL : https://hal.archives-ouvertes.fr/hal-01195942

Y. Bao, H. Fang, and J. Zhang, Topicmf: Simultaneously exploiting ratings and reviews for recommendation, AAAI, vol.14, pp.2-8, 2014.

S. Sra and I. S. Dhillon, Generalized nonnegative matrix approximations with bregman divergences, Advances in neural information processing systems, pp.283-290, 2006.

M. Niegowski and M. Zivanovic, Ecg-emg separation by using enhanced non-negative matrix factorization, Engineering in Medicine and Biology Society (EMBC), pp.4212-4215, 2014.
DOI : 10.1109/embc.2014.6944553

M. W. Berry, M. Browne, A. N. Langville, V. P. Pauca, and R. J. Plemmons, Algorithms and applications for approximate nonnegative matrix factorization, Computational statistics & data analysis, vol.52, issue.1, pp.155-173, 2007.
DOI : 10.1016/j.csda.2006.11.006
URL : http://www.wfu.edu/~plemmons/papers/BBLPP-rev.pdf

W. Press, B. Flannery, S. Teukolsky, and W. Vetterling, Numerical recepies in pascal: The art of scientific computing, pp.459-462, 1986.

R. G. Brown and P. Y. Hwang, Introduction to random signals and applied kalman filtering: with matlab exercises and solutions, p.1997, 1997.

G. Rilling, P. Flandrin, and P. Goncalves, On empirical mode decomposition and its algorithms, IEEE-EURASIP workshop on nonlinear signal and image processing, vol.3, pp.8-11, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00570628

E. Vincent, R. Gribonval, and C. Févotte, Performance measurement in blind audio source separation, IEEE transactions on audio, speech, and language processing, vol.14, pp.1462-1469, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00544230

R. Mcgill, J. W. Tukey, and W. A. Larsen, Variations of box plots, The American Statistician, vol.32, issue.1, pp.12-16, 1978.

H. Yang, Statistical analysis of synchrosqueezed transforms, 2017.

J. Xiao and P. Flandrin, Multitaper time-frequency reassignment for nonstationary spectrum estimation and chirp enhancement, IEEE Transactions on Signal Processing, vol.55, pp.2851-2860, 2007.
URL : https://hal.archives-ouvertes.fr/ensl-00111665

H. Wu, Y. Chan, Y. Lin, and Y. Yeh, Using synchrosqueezing transform to discover breathing dynamics from ecg signals, Applied and Computational Harmonic Analysis, vol.36, issue.2, pp.354-359, 2014.

H. Wu, S. Hseu, M. Bien, Y. R. Kou, and I. Daubechies, Evaluating physiological dynamics via synchrosqueezing: Prediction of ventilator weaning, IEEE Transactions on Biomedical Engineering, vol.61, issue.3, pp.736-744, 2014.

Y. Hu, X. Tu, F. Li, and G. Meng, Joint high-order synchrosqueezing transform and multi-taper empirical wavelet transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions, Sensors, vol.18, issue.1, p.150, 2018.

Z. Cai, M. Zhang, and Y. Liu, Sea-surface weak target detection scheme using a cultural algorithm aided time-frequency fusion strategy, IET Radar, Sonar & Navigation, 2018.

W. Liu, S. Cao, Z. Wang, K. Jiang, Q. Zhang et al., A novel approach for seismic time-frequency analysis based on high-order synchrosqueezing transform, IEEE Geoscience and Remote Sensing Letters, 2018.

E. Chassande-mottin and A. Pai, Best chirplet chain: Near-optimal detection of gravitational wave chirps, Physical review D, vol.73, issue.4, p.42003, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00107261

J. Terrien, C. Marque, and G. Germain, Ridge extraction from the time-frequency representation (tfr) of signals based on an image processing approach: application to the analysis of uterine electromyogram ar tfr, IEEE Transactions on Biomedical Engineering, vol.55, issue.5, pp.1496-1503, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00308659

D. Fourer, J. Harmouche, J. Schmitt, T. Oberlin, S. Meignen et al., The astres toolbox for mode extraction of non-stationary multicomponent signals, 2017 25th European Signal Processing Conference (EUSIPCO), pp.1130-1134, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01579903