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On Empirical Mode Decomposition and its AlgorithmsPowerPoint Presentation

On Empirical Mode Decomposition and its Algorithms

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### On Empirical Mode Decomposition and its Algorithms

### (p)reprints, Matlab codes and demoswww.ens-lyon.fr/~flandrin/emd.html

G. Rilling, P. Flandrin (Cnrs - Éns Lyon, France)

P. Gonçalvès (Inria Rhône-Alpes, France)

IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, Grado (I), June 9-11, 2003

outline

- Empirical Mode Decomposition (EMD) basics
- examples
- algorithmic issues
- elements of performance evaluation
- perspectives

basic idea

- « multimodal signal = fast oscillations on the top of slower oscillations »
- Empirical Mode Decomposition (Huang)
- identify locally the fastest oscillation
- substract to the signal and iterate on the residual
- data-driven method, locally adaptive and multiscale

Huang’s algorithm

- compute lower and upper envelopes from interpolations between extrema
- substract mean envelope from signal
- iterate until mean envelope = 0 and #{extrema} = #{zero-crossings} ± 1

- substract the obtained Intrinsic Mode Function (IMF) from signal and iterate on residual

EMD and AM-FM signals

- quasi-monochromatic harmonic oscillations
- self-adaptive time-variant filtering
- example : 2 sinus FM + 1 Gaussian wave packet

nonlinear oscillations

- IMF ≠ Fourier mode and, in nonlinear situations, 1 IMF = many Fourier modes
- example : 1 HF triangle + 1 MF tone + 1 LF triangle

issues

- algorithm ?
- intuitive but ad-hoc procedure, not unique
- several user-controlled tunings

- performance ?
- difficult evaluation since no analytical definition
- numerical simulations

algorithmic issues

- interpolation
- type ? cubic splines
- border effects ? mirror symmetry

- stopping criteria
- mean zero ? 2 thresholds
- variation 1 : « local EMD »

- computational burden
- about log2 N IMF ’s for N data points
- variation 2 : « on-line EMD »

performance evaluation

- extensive numerical simulations
- deterministic framework
- importance of sampling
- ability to resolve multicomponent signals

- a complement to stochastic studies
- noisy signals + fractional Gaussian noise
- PF et al., IEEE Sig. Proc. Lett., to appear

zero mean : tone = IMF

non zero mean : tone ≠ IMF

experiment 1

- 256 points tone, with 0 ≤ f ≤1/2
- error = normalized L2 distance comparing tone vs. IMF #1
- minimum when 1/f even multiple of the sampling period

experiment 2

- 256 points tones, with 0 ≤ f1 ≤1/2 and f2 ≤ f1
- error = weighted normalized L2 distance comparing tone #1 vs. IMF #1, and tone #2 vs. max{IMF #k, k ≥ 2}

experiment 3

- intertwining of amplitude ratio, sampling rate and frequency spacing
- dominant effect when f1 ≤ 1/4 : constant-Q (wavelet-like) « confusion band »

concluding remarks

- EMD is an appealing data-driven and multiscale technique
- spontaneous dyadic filterbank structure in « stationary » situations, stochastic (fGn) or not (tones)
- EMD defined as output of an algorithm: theoretical framework beyond numerical simulations?

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