Empirical Mode Decompositions, from basics to recent results

SpeakerPatrick Flandrin
Organization Laboratoire de Physique, Eciole Normale Superieure de Lyon, France
LocationEBII 1230
DateMarch 16, 2012 12:50 PM

Decomposing an observed signal into a combination of amplitude and frequency modulated orthogonal components is an ill-posed problem for which numerous solutions have been proposed in the past, based on various constraints. Amongst the most recent ones stand the empirical mode decomposition (EMD), whose conceptual simplicity and effectiveness in a number of applications make it appealing despite its merely empirical grounds. The purpose of this lecture is to give a comprehensive overview of EMD, from its original formulation to several extensions and/or variations that have been recently proposed, and to highlight pros and cons of the method.


Patrick Flandrin (F) received the engineer degree from ICPI Lyon, France, in 1978, and the Doct.-Ing. and Docteur d'Etat degrees from INP Grenoble, France, in 1982 and 1987, respectively. He joined CNRS in 1982, where he is currently a Research Director. Since 1991, he has been with the Signals, Systems, and Physics Group, Physics Department, Ecole Normale Supérieure de Lyon, France.

Prof. Flandrin has been a major contributor to the theory of (bilinear) Time-Frequency representations and non-stationary signal analysis. He played a major role in the developments of the wavelet theory and the analysis of fractional Brownian motion. Recently, he opened a new research direction studying the Empirical Mode Decomposition and revisiting stationarity with significant contributions on stationarity tests.

Prof. Flandrin is author of the book titled, Time-Frequency/Time-Scale Analysis and has authored more than 250 journal and conference proceeding research articles.

Prof. Flandrin received several research awards including Philip Morris Prize in Mathematics (1991); SPIE Wavelet Pioneer Award (2001); "Prix Michel Monpetit" from the French Academy of Sciences (2001); and Silver Medal from CNRS (2010).

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