Empirical Mode Decomposition as a Filter Bank

TLDR

Empirical mode decomposition (EMD) was pioneered by Huang et al. to adaptively represent non‑stationary signals as sums of zero‑mean amplitude‑modulation and frequency‑modulation components. The study aims to understand EMD behavior in stochastic broadband noise by conducting numerical experiments with fractional Gaussian noise. The authors performed numerical experiments using fractional Gaussian noise to analyze EMD performance.

Abstract

Empirical mode decomposition (EMD) has recently been pioneered by Huang et al. for adaptively representing nonstationary signals as sums of zero-mean amplitude modulation frequency modulation components. In order to better understand the way EMD behaves in stochastic situations involving broadband noise, we report here on numerical experiments based on fractional Gaussian noise. In such a case, it turns out that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet decompositions. It is also pointed out that the hierarchy of the extracted modes may be similarly exploited for getting access to the Hurst exponent.