Bivariate Empirical Mode Decomposition

Abstract : The Empirical Mode Decomposition (EMD) has been introduced quite recently to adaptively decompose nonstationary and/or nonlinear time series. The method being initially limited to real-valued time series, we propose here an extension to bivariate (or complex-valued) time series which generalizes the rationale underlying the EMD to the bivariate framework. Where the EMD extracts zero-mean oscillating components, the proposed bivariate extension is designed to extract zero-mean rotating components. The method is illustrated on a real-world signal and properties of the output components are discussed. Free Matlab/C codes are available at http://perso.ens-lyon.fr/patrick.flandrin.
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Pré-publication, Document de travail
10 pages, 3 figures. Submitted to Signal Processing Letters, IEEE. Matlab/C codes and additional .. 2007
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Soumis le : mardi 20 mars 2007 - 17:20:42
Dernière modification le : mardi 24 avril 2018 - 13:52:26
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  • HAL Id : ensl-00137611, version 1

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Gabriel Rilling, Patrick Flandrin, Paulo Gonçalves, Jonathan M. Lilly. Bivariate Empirical Mode Decomposition. 10 pages, 3 figures. Submitted to Signal Processing Letters, IEEE. Matlab/C codes and additional .. 2007. 〈ensl-00137611〉

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