Visible to the public An adaptive threshold de-noising method based on EEMD

TitleAn adaptive threshold de-noising method based on EEMD
Publication TypeConference Paper
Year of Publication2014
AuthorsJialing Mo, Qiang He, Weiping Hu
Conference NameSignal Processing, Communications and Computing (ICSPCC), 2014 IEEE International Conference on
Date PublishedAug
KeywordsAdaptive, adaptive determination correctness analysis, adaptive determination correctness verification, adaptive threshold de-noising method, autocorrelation method, Correlation, correlation theory, cross-correlation method, de-noised layer component, de-noising signal reconstruction, decomposition level selection, EEMD, Empirical mode decomposition, ensemble empirical mode decomposition, filtering theory, mode mixing problem, noise dominant layer filtering, noise reduction, signal denoising, signal reconstruction, Signal to noise ratio, signal-to-noise boundary layer, Speech, threshold de-noised signal dominant layer, Threshold De-noising, wavelet analysis, wavelet base selection, wavelet threshold, wavelet transforms, wavelet-based de-noising method, White noise

In view of the difficulty in selecting wavelet base and decomposition level for wavelet-based de-noising method, this paper proposes an adaptive de-noising method based on Ensemble Empirical Mode Decomposition (EEMD). The autocorrelation, cross-correlation method is used to adaptively find the signal-to-noise boundary layer of the EEMD in this method. Then the noise dominant layer is filtered directly and the signal dominant layer is threshold de-noised. Finally, the de-noising signal is reconstructed by each layer component which is de-noised. This method solves the problem of mode mixing in Empirical Mode Decomposition (EMD) by using EEMD and combines the advantage of wavelet threshold. In this paper, we focus on the analysis and verification of the correctness of the adaptive determination of the noise dominant layer. The simulation experiment results prove that this de-noising method is efficient and has good adaptability.

Citation Key6986184