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  • Research Article
  • Open Access

Speech Enhancement via EMD

  • 1, 2,
  • 2, 3Email author,
  • 2, 3 and
  • 1
EURASIP Journal on Advances in Signal Processing20082008:873204

  • Received: 13 August 2007
  • Accepted: 5 March 2008
  • Published:


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.


  • Shrinkage
  • Speech Signal
  • Noise Reduction
  • Additive White Gaussian Noise
  • Empirical Mode Decomposition

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Authors’ Affiliations

Unité Signaux et Systèmes, ENIT, BP 37, Le Belvédère, Tunis, 1002, Tunisia
IRENav, Ecole Navale, Lanvéoc Poulmic, BP600, 29200 Brest-Armées, France
E3I2, EA 3876, ENSIETA, 2 rue François Verny, 29806 Brest Cedex 09, France


© Kais Khaldi et al. 2008

This article is published under license to BioMed Central Ltd. 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.