Speech Enhancement via EMD. (5th June 2008)

• Record Type: Journal Article
• Authors: Khaldi Khaldi, Kais Kais; Boudraa Boudraa, Abdel-Ouahab Abdel-Ouahab; Bouchikhi Bouchikhi, Abdelkhalek Abdelkhalek; Alouane Alouane, Monia Turki-Hadj Monia Turki-Hadj
• Other Names: Attoh-Okine Attoh-Okine Nii Nii Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2008(2008)
• Publication Date: 2008-06-05
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2008/873204
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : In this study, two new approaches for speech signal noise reduction based on the empirical mode decomposition (EMD) recently introduced by Huang et al. (1998) are proposed. Based on the EMD, both reduction schemes are fully data-driven approaches. Noisy signal is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs), using a temporal decomposition called sifting process. Two strategies for noise reduction are proposed: filtering and thresholding. The basic principle of these two methods is the signal reconstruction with IMFs previously filtered, using the minimum mean-squared error (MMSE) filter introduced by I. Y. Soon et al. (1998), or thresholded using a shrinkage function. The performance of these methods is analyzed and compared with those of the MMSE filter and wavelet shrinkage. The study is limited to signals corrupted by additive white Gaussian noise. The obtained results show that the proposed denoising schemes perform better than the MMSE filter and wavelet approach.