Carrier Frequencies, Holomorphy, and Unwinding

We prove that functions of intrinsic-mode type (a classical models for signals) behave essentially like holomorphic functions: adding a pure carrier frequency $e^{int}$ ensures that the anti-holomorphic part is much smaller than the holomorphic part $ \| P_{-}(f)\|_{L^2} \ll \|P_{+}(f)\|_{L^2}.$ This enables us to use techniques from complex analysis, in particular the \textit{unwinding series}. We study its stability and convergence properties and show that the unwinding series can stabilize and show that the unwinding series can provide a high resolution time-frequency representation, which is robust to noise.

[1]  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.

[2]  Simon Haykin,et al.  The chirplet transform: physical considerations , 1995, IEEE Trans. Signal Process..

[3]  Su Li,et al.  Wave-shape function analysis - when cepstrum meets time-frequency analysis , 2016, ArXiv.

[4]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[5]  D. Gabor,et al.  Theory of communication. Part 1: The analysis of information , 1946 .

[6]  Patrick Flandrin,et al.  Making reassignment adjustable: The Levenberg-Marquardt approach , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[8]  Hau-Tieng Wu,et al.  Non‐parametric and adaptive modelling of dynamic periodicity and trend with heteroscedastic and dependent errors , 2014 .

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

[10]  Xuefeng Chen,et al.  A Frequency-Shift Synchrosqueezing Method for Instantaneous Speed Estimation of Rotating Machinery , 2015 .

[11]  Nelly Pustelnik,et al.  Empirical Mode Decomposition revisited by multicomponent non smooth convex optimization 1 , 2014 .

[12]  I. Mazin,et al.  Theory , 1934 .

[13]  Haomin Zhou,et al.  Adaptive Local Iterative Filtering for Signal Decomposition and Instantaneous Frequency analysis , 2014, 1411.6051.

[14]  Tao Qian Intrinsic mono‐component decomposition of functions: An advance of Fourier theory , 2010 .

[15]  Charles K. Chui,et al.  Signal decomposition and analysis via extraction of frequencies , 2016 .

[16]  Li Su,et al.  Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape Short Time Fourier Transform and Nonlocal Median , 2016, Front. Appl. Math. Stat..

[17]  M. Weiss,et al.  A derivation of the main results of the theory of Hp spaces , 1962 .

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

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

[20]  Bruno Torrésani,et al.  An optimally concentrated Gabor transform for localized time-frequency components , 2014, Adv. Comput. Math..

[21]  Hau-Tieng Wu,et al.  Evaluating Physiological Dynamics via Synchrosqueezing: Prediction of Ventilator Weaning , 2013, IEEE Transactions on Biomedical Engineering.

[22]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

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

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

[25]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[26]  Z. Nehari Bounded analytic functions , 1950 .

[27]  Gonzalo Galiano,et al.  On a non-local spectrogram for denoising one-dimensional signals , 2013, Appl. Math. Comput..

[28]  Patrick Flandrin,et al.  Time-Frequency/Time-Scale Analysis , 1998 .

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

[30]  Ronald R. Coifman,et al.  Nonlinear Phase Unwinding of Functions , 2015, 1508.01241.

[31]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[32]  Hau-Tieng Wu,et al.  Assess Sleep Stage by Modern Signal Processing Techniques , 2014, IEEE Transactions on Biomedical Engineering.

[33]  Ronald R. Coifman,et al.  Phase evaluation and segmentation , 2000 .

[34]  Stéphane Mallat,et al.  Group Invariant Scattering , 2011, ArXiv.

[35]  Thomas Y. Hou,et al.  Extraction of Intrawave Signals Using the Sparse Time-Frequency Representation Method , 2014, Multiscale Model. Simul..

[36]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[37]  Balth. van der Pol,et al.  The Fundamental Principles of Frequency Modulation , 1946 .

[38]  Nicki Holighaus,et al.  Theory, implementation and applications of nonstationary Gabor frames , 2011, J. Comput. Appl. Math..

[39]  Julián Velasco Valdés,et al.  On a non-local spectrogram for denoising one-dimensional signals , 2014, Appl. Math. Comput..

[40]  Hau-tieng Wu,et al.  Nonparametric and adaptive modeling of dynamic seasonality and trend with heteroscedastic and dependent errors , 2012, 1210.4672.

[41]  Patrick Flandrin,et al.  Time–Frequency Filtering Based on Spectrogram Zeros , 2015, IEEE Signal Processing Letters.