Speech signal noise reduction by EMD
Kais Khaldi, Abdel‐Ouahab Boudraa, Abdelkhalek Bouchikhi, M. Turki-Hadj Alouane, El Hadji S. Diop · 2008
In this paper, a speech signal noise reduction based on a multiresolution approach referred to as Empirical Mode Decomposition (EMD) [1] is introduced. The proposed speech denoising method is a fully data-driven approach. Noisy signal is decomposed adaptively into oscillatory components called Intrinsic Mode Functions (IMFs), using a temporal decomposition called sifting process. The basic principle of the method is to reconstruct the signal with IMFs previously thresholded using a shrinkage function. The denoising method is applied to speech with different noise levels and the results are compared to wavelet shrinkage. The study is limited to signals corrupted by additive white Gaussian noise.