KHALDI, Kais; BOUDRAA, Abdel; BOUCHIKHI, A.; ALOUANE, M. T.-H.

doi:10.1155/2008/873204

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KHALDI, Kais
Institut de Recherche de l'Ecole Navale [IRENAV]

BOUDRAA, Abdel
Extraction et Exploitation de l'Information en Environnements Incertains [E3I2]

BOUCHIKHI, A.
Extraction et Exploitation de l'Information en Environnements Incertains [E3I2]

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EURASIP Journal on Advances in Signal Processing. 2008-06-01

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Résumé en anglais

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.< Réduire

Mots clés en anglais

Additive White Gaussian Noise

Empirical Mode Decomposition

Noise Reduction

Shrinkage

Speech Signal

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