Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30372
EMD-Based Signal Noise Reduction

Authors: A.O. Boudraa, Z. Saidi, J.C. Cexus


This paper introduces a new signal denoising based on the Empirical mode decomposition (EMD) framework. The method is a fully data driven approach. Noisy signal is decomposed adaptively into oscillatory components called Intrinsic mode functions (IMFs) by means of a process called sifting. The EMD denoising involves filtering or thresholding each IMF and reconstructs the estimated signal using the processed IMFs. The EMD can be combined with a filtering approach or with nonlinear transformation. In this work the Savitzky-Golay filter and shoftthresholding are investigated. For thresholding, IMF samples are shrinked or scaled below a threshold value. The standard deviation of the noise is estimated for every IMF. The threshold is derived for the Gaussian white noise. The method is tested on simulated and real data and compared with averaging, median and wavelet approaches.

Keywords: empirical mode decomposition, Signal denoisingnonstationary process

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3312


[1] J.G. Proakis, and D.G. Manolakis, Digital Signal Processing: Principles,Algorithms, and Applications (3rd edition), Prentice-Hall, 1996.
[2] D.L. Donoho, and I.M. Johnstone, "Ideal spatial adaption via wavelet via wavelet shrinkage," Biometrica, vol. 81, pp. 425-455, 1994.
[3] D.L. Donoho, "De-noising by soft-thresholding," IEEE Trans. Inform.Theory, vol. 41, no. 3, pp. 613-627, 1995.
[4] N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shin, Q. Zheng, N.C.Yen, C.C. Tung, and H.H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time seriesanalysis," Proc. Royal Soc. London A, vol. 454, pp. 903-995, 1998.
[5] P. Flandrin, G. Rilling, and P. Gonçalves, "Empirical Mode Decomposition as a Filter Bank," IEEE Sig. Proc. Lett., vol. 11, no. 2,pp. 112-114, 2004.
[6] A. Savitzky, and M.J.E. Golay, "Smoothing and differentiation, of data by simplified least squares procedures," Analytical chemistry, vol. 36,pp. 1627-1639, 1964.
[7] A. Antoniadis, J. Bigot, and T. Sapatinas, "Wavelet estimators in nonparametric regression: a comparative simulation," study, J. Statist. Software, vol. 6, no. 6, pp. 1-83, 2001.
[8] D.L. Donoho, and I.M. Johnstone, "Ideal spatial adaptation by wavelet shrinkage, " Biometrika, vol. 81, no. 3, pp. 425-455, 1994.