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Speech Enhancement via EMD

  • Kais Khaldi
  • Abdel-Ouahab Boudraa
  • Abdelkhalek Bouchikhi
  • Monia Turki-Hadj Alouane
Open Access
Research Article
Part of the following topical collections:
  1. The Empirical Mode Decomposition and the Hilbert-Huang Transform

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.

Keywords

Shrinkage Speech Signal Noise Reduction Additive White Gaussian Noise Empirical Mode Decomposition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Publisher note

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Copyright information

© 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.

Authors and Affiliations

  • Kais Khaldi
    • 1
    • 2
  • Abdel-Ouahab Boudraa
    • 2
    • 3
  • Abdelkhalek Bouchikhi
    • 2
    • 3
  • Monia Turki-Hadj Alouane
    • 1
  1. 1.Unité Signaux et Systèmes, ENITLe Belvédère, TunisTunisia
  2. 2.IRENav, Ecole Navale, Lanvéoc PoulmicBrest-ArméesFrance
  3. 3.E3I2, EA 3876, ENSIETABrest Cedex 09France

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