Speech Enhancement via EMD
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Research Article Speech Enhancement via EMD Kais Khaldi,1, 2 Abdel-Ouahab Boudraa,2, 3 Abdelkhalek Bouchikhi,2, 3 and Monia Turki-Hadj Alouane1 1 Unit´ e
Signaux et Syst`emes, ENIT, BP 37, Le Belv´ed`ere 1002, Tunis, Tunisia Ecole Navale, Lanv´eoc Poulmic, BP600, 29200 Brest-Arm´ees, France 3 E3I2, EA 3876, ENSIETA, 2 rue Franc ¸ois Verny, 29806 Brest Cedex 09, France 2 IRENav,
Correspondence should be addressed to Abdel-Ouahab Boudraa, [email protected] Received 13 August 2007; Accepted 5 March 2008 Recommended by Nii Attoh-Okine 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. Copyright © 2008 Kais Khaldi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
Speech enhancement is a classical problem in signal processing, particularly in the case of additive white Gaussian noise where different noise reduction methods have been proposed [1–4]. When noise estimation is available, then filtering gives accurate results. However, these methods are not so effective when noise is difficult to estimate. Linear methods such as Wiener filtering [5] are used because linear filters are easy to implement and design. These linear methods are not so effective for signals presenting sharp edges or impulses of short duration. Furthermore, real signals are often nonstationary. In order to overcome these shortcomings, nonlinear methods have been proposed and especially those based on wavelets thresholding [6, 7]. The idea of wavelet thresholding relies on the assumption that signal magnitudes dominate the magnitudes of noise in a wavelet representation so that wavelet coefficients can be set to zero if their magnitudes are less than a predetermined threshold [7]. A limit of the wavelet approach is that basis functions are fixed, and, thus, do not necessarily match all real signals. To avoid this problem, time-frequency atomic signal decomposition can be used [8, 9]. As for wavelet packets
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