Empirical mode decomposition, fractional Gaussian noise and Hurst exponent estimation

Author

Rilling, Gabriel ; Flandrin, Patrick ; Goncalves, Paulo

Author_Institution

Lab. de Phys., Ecole Normale Superieure de Lyon, France

Volume

4

fYear

2005

fDate

18-23 March 2005

Abstract

Huang´s data-driven technique of empirical mode decomposition (EMD) is applied to the versatile, broadband, model of fractional Gaussian noise (fGn). The spectral analysis and statistical characterization of the obtained modes reveal an equivalent filter bank structure together with gamma distributed variances, both sharing some properties with wavelet decompositions. These common features are then used to mimic wavelet based techniques aimed at estimating the Hurst exponent.

Keywords

Gaussian noise; adaptive signal processing; gamma distribution; spectral analysis; statistical analysis; EMD; Hurst exponent estimation; adaptive decomposition; broadband noise model; empirical mode decomposition; equivalent filter bank structure; fractional Gaussian noise; gamma distributed variances; scaling exponents; spectral analysis; statistical characterization; wavelet decomposition properties; 1f noise; Character generation; Filter bank; Frequency; Gaussian noise; Nonlinear systems; Signal design; Signal processing; Spectral analysis; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-8874-7

Type

conf

DOI

10.1109/ICASSP.2005.1416052

Filename

1416052