Table of Contents Author Guidelines Submit a Manuscript
Advances in Acoustics and Vibration
Volume 2014, Article ID 765454, 11 pages
http://dx.doi.org/10.1155/2014/765454
Research Article

Subband DCT and EMD Based Hybrid Soft Thresholding for Speech Enhancement

1Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
2Department of Computer Science and Engineering, The University of Rajshahi, Rajshahi 6205, Bangladesh
3Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
4Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh

Received 5 February 2014; Accepted 17 April 2014; Published 20 May 2014

Academic Editor: Rama B. Bhat

Copyright © 2014 Erhan Deger 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.

Linked References

  1. J. R. Deller, J. G. Proakis, and J. H. L. Hansen, Discrete-Time Processing of Speech Signals, IEEE Press, New York, NY, USA, 2000.
  2. D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Bahoura and J. Rouat, “Wavelet speech enhancement based on the Teager energy operator,” IEEE Signal Processing Letters, vol. 8, no. 1, pp. 10–12, 2001. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Salahuddin, S. Z. Al Islam, M. K. Hasan, and M. R. Khan, “Soft thresholding for DCT speech enhancement,” Electronics Letters, vol. 38, no. 24, pp. 1605–1607, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and Hilbert spectrum for non-linear and non-stationary time series analysis,” Proceedings of the Royal Society A, vol. 454, pp. 903–995, 1998. View at Publisher · View at Google Scholar
  6. P. Flandrin, G. Rilling, and P. Gonçalvés, “Empirical mode decomposition as a filter bank,” IEEE Signal Processing Letters, vol. 11, no. 2, pp. 112–114, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. M. C. Ivan and G. B. Richard, “Empirical mode decomposition based frequency attributes,” in Proceedings of the 69th SEG Meeting, Houston, Tex, USA, 1999.
  8. Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1–41, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. D. P. Madic, N. U. Rehman, Z. Wu, and N. E. Huang, “Empirical mode decomposition based time-frequency analysis of multivariate signals: the power of adaptive data analysis,” IEEE Signal Processing Magazine, vol. 30, no. 6, pp. 74–86, 2013. View at Google Scholar
  10. N. U. Rehman, C. Park, N. E. Huang, and D. P. Mandic, “EMD via MEMD: multivariate noise-aided computation of standard EMD,” Advances in Adaptive Data Analysis, vol. 5, no. 2, pp. 1–25, 2013. View at Google Scholar
  11. M. K. Hasan, M. S. A. Zilany, and M. R. Khan, “DCT speech enhancement with hard and soft thresholding criteria,” Electronics Letters, vol. 38, no. 13, pp. 669–670, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. A. W. Rix, J. G. Beerends, M. P. Hollier, and A. P. Hekstra, “Perceptual evaluation of speech quality (PESQ)—a new method for speech quality assessment of telephone networks and codecs,” in Proceedings of the IEEE Interntional Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 749–752, May 2001. View at Scopus