A Study of Applying Ensemble Empirical Mode Decomposition to Signal Noise Reduction
Date Issued
2011
Date
2011
Author(s)
Wang, Ying-Chung
Abstract
A signal analyzing method, Hilbert-Huang Transform (HHT), was proposed by Norden E. Huang et al. in 1998. By using Emprical Mode Decomposition (EMD), signal could be decomposed into a finite number of intrinsic mode functions (IMFs) based on the local characteristic time-scale of the signal. Devoting these IMFs with Hilbert Transform could obtain meaningful instantaneous information about the signal.
In this thesis, Ensemble Empirical Mode Decomposition (EEMD) and the post-processing of EEMD that were improved from the original EMD were involved to reduce the noise contained in the signal. By using the characteristic of EMD, the "Mutual Information" by calculating the entropy of signal from Independent Component Analysis was used to reduce the noisy component at first filtering, and a threshold-filtering selection method adapted to IMFs filtered the signal at second try. Adaptive Center-Weighted Mean Filter was then used to reduce the rest noisy component in the signal. Such attempting of triple-filtering could success removing most noisy component inside the IMFs that was generated by post-processing of Ensemble Empirical Mode Decomposition.
The proposed method was tested by 4 test signals and 2 voice signals added with various level of noise under simulation experiment. From the simulation result, compared with wavelets and other existing method, the proposed method had better performance of de-noising in low SNR circumstances. The proposed method could retain more information of the signal with less destruction in de-noising process, and take into account the noise reduction with a better robustness and stability.
Subjects
EEMD
post-processing of EEMD
noise reduction
Mutual Information
HHT
Type
thesis
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