Speech denoising by Adaptive Weighted Average filtering in the EMD framework

This paper introduces a new speech enhancement method, which combines Adaptive Center Weighted Average (ACWA) filter with Empirical Mode Decomposition (EMD). Both ACWA and EMD operate in the time domain. The ACWA filter is advantageous as it operates adaptively in the time domain and does not require the stationarity and the whiteness of the signals. Thanks to the data driven decomposition of the EMD, the application of the ACWA filter on the IMFs gives better results than the ACWA filtering of the noisy signal. The proposed EMD-ACWA denoising method is applied to noisy speech signal with different noise levels and the results are compared to those obtained by two different denoising methods: wavelet thresholds and ACWA filtering. A significant superiority of the EMD-ACWA method over the two others is shown in white noisy contexts as well as in correlated noisy ones.