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On Empirical Mode Decomposition and its Algorithms

On Empirical Mode Decomposition and its Algorithms. G. Rilling, P. Flandrin (Cnrs - Éns Lyon, France) P. Gonçalvès (Inria Rhône-Alpes, France). outline. Empirical Mode Decomposition (EMD) basics examples algorithmic issues elements of performance evaluation perspectives. basic idea.

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On Empirical Mode Decomposition and its Algorithms

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  1. On Empirical Mode Decomposition and its Algorithms 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

  2. outline • Empirical Mode Decomposition (EMD) basics • examples • algorithmic issues • elements of performance evaluation • perspectives

  3. 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

  4. 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

  5. how EMD works

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