Application of EMD Denoising Approach in Noisy Blind Source Separation

Blind Source Separation (BSS) algorithms based on the noise-free model are not applicable when the Signal Noise Ratio (SNR) is low. In view of this situation, our solution is to denoise the mixtures with additive white Gaussian noise firstly, and then use BSS algorithms. This paper proposes a piecewise Empirical Mode Decomposition (EMD) thresholding approach to denoise mixtures with strong noise. This approach can distinguish the noise-dominated IMFs and signal-dominated IMFs, and then respectively apply different thresholdings methods. Simulation results show that compared with the Wavelet denoising, the proposed approach has a better denoising performance, and can remarkably enhance the separation performance of BSS algorithms, especially when the signal SNR is low.

[1]  Binwei Weng,et al.  ECG Denoising Based on the Empirical Mode Decomposition , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Gabriel Rilling,et al.  One or Two Frequencies? The Empirical Mode Decomposition Answers , 2008, IEEE Transactions on Signal Processing.

[3]  A. Boudraa,et al.  A new EMD denoising approach dedicated to voiced speech signals , 2008, 2008 2nd International Conference on Signals, Circuits and Systems.

[4]  Steve McLaughlin,et al.  Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding , 2009, IEEE Transactions on Signal Processing.

[5]  A. Boudraa,et al.  EMD-Based Signal Noise Reduction , 2005 .

[6]  Pierre Comon,et al.  Robust Independent Component Analysis by Iterative Maximization of the Kurtosis Contrast With Algebraic Optimal Step Size , 2010, IEEE Transactions on Neural Networks.

[7]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[8]  G. Tsolis,et al.  Signal Denoising Using Empirical Mode Decomposition and Higher Order Statistics , 2011 .

[9]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[10]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[11]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[12]  Christian Jutten,et al.  Source separation in strong noisy mixtures: A study of wavelet de-noising pre-processing , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[13]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[14]  Babak Hossein Khalaj,et al.  Grey Prediction Based Handoff Algorithm , 2007 .

[15]  G. Tsolis,et al.  Seismo-ionospheric coupling correlation analysis of earthquakes in Greece, using empirical mode decomposition , 2009 .

[16]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[17]  Jean-Christophe Cexus,et al.  Denoising via empirical mode decomposition , 2006 .

[18]  Gabriel Rilling,et al.  EMD Equivalent Filter Banks, from Interpretation to Applications , 2005 .