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Speech Enhancement Using ICA with EMD-Based Reference

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Independent Component Analysis and Blind Signal Separation (ICA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3889))

Abstract

Different from the traditional ICA that recovers all the source signals simultaneously, the ICA with reference (ICA-R) extracts only some desired source signals from the mixtures of source signals by incorporating some a priori information into the separation process. This paper applies ICA-R to extracting a target speech signal from its noisy linear mixtures by constructing a proper reference signal with the empirical mode decomposition (EMD). Specifically, EMD is used to obtain an approximate envelope of the power spectrum of the desired speech, which is quite different from the power spectra of the environmental noises. The results of computer simulations and performance analyses demonstrate the efficiency of the proposed method.

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References

  1. Comon, P.: Independent component analysis, a new concept? Signal Processing 36(3), 287–314 (1994)

    Article  MATH  Google Scholar 

  2. Cardoso, J.F.: Blind signal separation: statistical principles. Proc. of the IEEE 86(10), 2009–2025 (1998)

    Article  Google Scholar 

  3. Amari, S., Cichocki, A.: Adaptive blind signal processing - Neural network approaches. Proc. of the IEEE 86(10), 2026–2048 (1998)

    Article  Google Scholar 

  4. Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Networks 13(4-5), 411–430 (2000)

    Article  Google Scholar 

  5. Barros, A.K., Rutkowski, T., Itakura, F., Ohnishi, N.: Estimation of speech embedded in a reverberant and noisy environment by independent component analysis and wavelets. IEEE Transactions on Neural Networks 13(4), 888–893 (2002)

    Article  Google Scholar 

  6. Visser, E., Lee, T.W.: Speech enhancement using blind source separation and two-channel energy based speaker detection. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2003), vol. 1, pp. 884–887 (2003)

    Google Scholar 

  7. Lee, J.H., Jung, H.Y., Lee, T.W., Lee, S.Y.: Speech enhancement with MAP estimation and ICA-based speech features. Electronics Letters 36(17), 1506–1507 (2000)

    Article  Google Scholar 

  8. Lu, W., Rajapakse, J.C.: Constrained Independent Component Analysis. Advance in Neural Information Processing Systems, pp. 570–576. MIT Press, Cambridge (2000)

    Google Scholar 

  9. Lu, W., Rajapakse, J.C.: ICA with reference. In: Proc. Third Int. Conf. on ICA and Blind Source Separation (ICA 2001), pp. 120–125 (2001)

    Google Scholar 

  10. Hyvärinen, A.: New approximations of differential entropy for independent component analysis and projection pursuit. In: Advances in Neural Information Processing Systems 10 (NIPS 1997), pp. 273–279 (1997)

    Google Scholar 

  11. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. Royal Soc. Lond. A 454, 903–995 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  12. Liang, H.L., Lin, Q.H., Chen, J.D.Z.: Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease. IEEE Transactions on Biomedical Engineering 52(10), 1692–1701 (2005)

    Article  Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Zheng, Y., Lin, Q., Yin, F., Liang, H. (2006). Speech Enhancement Using ICA with EMD-Based Reference. 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_92

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  • DOI: https://doi.org/10.1007/11679363_92

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32630-4

  • Online ISBN: 978-3-540-32631-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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