Abstract
It is well known that the non-stationary noise is the most difficult to be removed in speech enhancement. In this paper a novel speech enhancement algorithm based on the empirical mode decomposition (EMD) and then ICA is proposed to suppress the non-stationary noise. The noisy speech is decomposed into components by the EMD and ICA-based vector space, and the components are processed and reconstructed, respectively, by distinguishing between voiced speech and unvoiced speech. There are no requirements of noise whitening and SNR pre-calculating. Experiments show that the proposed method performs well suppressing of the non-stationary noise in short-wave channel for speech enhancement.
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References
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)
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)
Zheng, W.T., Cao, Z.H.: Speech enhancement based on MMSE-STSA estimation and residual noise reduction. In: IEEE Region 10 International Conference on EC3-Energy, Computer, Communication and Control Systems, vol. 3, pp. 265–268 (1991)
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)
Lim, J.S., Oppenheim, A.V.: Enhancement and bandwidth compression of noisy speech. Proc. of the IEEE 67, 1586–1604 (1979)
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)
He, C., Zweig, Z.: Adaptive two-band spectral subtraction with multi-window spectral estimation. In: ICASSP, vo. 2, pp. 793–796 (1999)
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)
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)
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)
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© 2006 Springer-Verlag Berlin Heidelberg
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Shen, LR., Li, XY., Yin, QB., Wang, HQ. (2006). Speech Enhancement in Short-Wave Channel Based on ICA in Empirical Mode Decomposition Domain. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_88
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DOI: https://doi.org/10.1007/11679363_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
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