Abstract—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.

Index Terms—Signal denoising; empirical mode decomposition (EMD); wavelet transform (WT); waveshrink algorithm; noisy blind source separation.

Cite: Wei Wu and Hua Peng, "Application of EMD Denoising Approach in Noisy Blind Source Separation," Journal of Communications, vol. 9, no. 6, pp. 506-514, 2014. Doi: 10.12720/jcm.9.6.506-514