Empirical mode decomposition, fractional Gaussian noise and hurst exponent estimation

被引：0

作者：

Rilling, G ^{[1
]}

Flandrin, P ^{[1
]}

Gonçalvès, P ^{[1
]}

机构：

[1] Ecole Normale Super Lyon, CNRS, UMR 5672, Phys Lab, F-69364 Lyon, France

来源：

2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：489 / 492

页数：4

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