Abstract
This paper introduces a new voiced speech denoising approach based on the Empirical Mode Decomposition (EMD) associated to an appropriate sifting process. Noisy signal is decomposed adaptively into intrinsic oscillatory components called (IMFs). Since, the energy of a voiced speech signal is distributed over low and medium frequencies, the obtained lower order IMFs (high frequency components) are expected to be highly contaminated by noise. However, the last IMFs (low and medium frequency components) corresponding to the most structures of the signal, contain very low noise levels. Therefore the filtering of the low order IMFs may introduce a signal distortion rather than reducing noise. The principle of the proposed method is then based on filtering only the first IMFs considered very noisy by the Adaptive Center Weighted Average( ACWA) Filter. A criterion based on the IMFss energies is used to select the very noisy IMFs that must be filtered. The denoising proposed method is applied successfully to voiced speech signal corrupted with additive white gaussian noise. The reported results obtained for different noise levels, demonstrate the efficiency of the proposed method for reducing noise and its superiority over other denoising methods considered for comparison.