Speech Enhancement in Short-Wave Channel Based on Empirical Mode Decomposition

  • Li-Ran Shen
  • Qing-Bo Yin
  • Xue-Yao Li
  • Hui-Qiang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3967)


A novel speech enhancement method based on empirical mode decomposition is proposed. The method is a fully data driven approach. Noisy speech signal is decomposed adaptively into oscillatory components called Intrinsic Mode Functions (IMFs) using a process called sifting. The empirical mode decomposition denoising involves thresholding each IMFs. A nonlinear function is introduced for amplitude thresholding. And then reconstructs the estimated speech signal using the processed IMFs. The experimental results show significant improvement in output SNR and quality as compared to recently reported results.


Speech Signal Empirical Mode Decomposition Minimum Mean Square Error Speech Enhancement Clean Speech 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Martin, R.: Noise Power Spectral Density Estimation Based on Optimal Smoothing and Minimum Statistics. IEEE Trans. on Speech and Audio Processing 9, 504–512 (2001)CrossRefGoogle Scholar
  2. 2.
    Ephraim, Y., Malah, D.: Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator. IEEE Transactions on Acoustics, Speech and Signal Processing 32, 1109–1121 (1984)CrossRefGoogle Scholar
  3. 3.
    Zheng, W.T., Cao, Z.H.: Speech enhancement based on MMSE-STSA estimation and residual noise reduction. In: 1991 IEEE Region 10 International Conference on EC3-Energy, Computer, Communication and Control Systems, vol. 3, pp. 265–268 (1991)Google Scholar
  4. 4.
    Zhibin, L., Naiping, X.: Speech enhancement based on minimum mean-square error short-time spectral estimation and its realization. In: IEEE International conference on intelligent processing system, vol. 28, pp. 1794–1797 (1997)Google Scholar
  5. 5.
    Lim, J.S., Oppenheim, A.V.: Enhancement and bandwidth compression of noisy speech. Proc. of the IEEE 67, 1586–1604 (1979)CrossRefGoogle Scholar
  6. 6.
    Goh, Z., Tan, K., Tan, T.: Postprocessing method for suppressing musical noise generated by spectral subtraction. IEEE Trans. Speech Audio Procs 6, 287–292 (1998)CrossRefGoogle Scholar
  7. 7.
    He, C., Zweig, Z.: Adaptive two-band spectral subtraction with multi-window spectral estimation. In: ICASSP, vol. 2, pp. 793–796 (1999)Google Scholar
  8. 8.
    Huang, N.E.: The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis. J. Proc. R. Soc. Lond. A 454, 903–995 (1998)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Huang, W., Shen, Z., Huang, N.E., Fung, Y.C.: Engineering Analysis of Biological Variables: an Example of Blood Pressure over 1 Day. Proc. Natl. Acad. Sci. USA 95, 4816–4821 (1998)CrossRefGoogle Scholar
  10. 10.
    Huang, W., Shen, Z., Huang, N.E., Fung: Nonlinear Indicial Response of Complex Nonstationary Oscillations as Pulmonary Pretension Responding to Step Hypoxia. Proc. Natl. Acad. Sci, USA 96, 1833–1839 (1999)Google Scholar
  11. 11.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Li-Ran Shen
    • 1
  • Qing-Bo Yin
    • 1
  • Xue-Yao Li
    • 1
  • Hui-Qiang Wang
    • 1
  1. 1.The College Of Computer Science and TechnologyHarbin Engineering UniversityHarbinChina

Personalised recommendations