Higher-Order Feature Extraction of Non-Gaussian Acoustic Signals Using GGM-Based ICA

  • Wei Kong
  • Bin Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


In this paper, independent component analysis (ICA) is applied for feature extraction of non-Gaussian acoustic signals. The generalized Gaussian model (GGM) is introduced as the p.d.f. estimator in ICA because it can provide a general method for modeling non-Gaussian statistical structure of univariate distributions. It is demonstrated that the proposed method can efficiently extract ICA features for not only sup-Gaussian but also sub-Gaussian signals. The basis vectors are localized in both time and frequency domain and the resulting coefficients are statistically independent and sparse. The experiments of Chinese speech and the underwater signals show that the proposed method is more efficient than conventional methods.


Independent Component Analysis Independent Component Analysis Independent Component Analysis Algorithm Independent Component Analysis Method Chinese 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.
    Lee, J.-H., Jung, H.-J., Lee, T.-W.: Speech Coding and Noise Reduction Using ICA-based Speech Features. In: International Workshop on Independent Component Analysis (ICA 2000), Helsinki, pp. 417–422 (2000)Google Scholar
  2. 2.
    Jang, G.-J., Lee, T.-W.: Learning Statistically Efficient Features for Speaker Recognition. Neurocomputing 49, 329–348 (2002)CrossRefGoogle Scholar
  3. 3.
    Bell, A.J., Sejnowski, T.J.: Learning the Higher-Order Structure of a Nature Sound. Network: Computation in Neural System 7, 261–266 (1996)MATHCrossRefGoogle Scholar
  4. 4.
    Lee, J.-H., Jung, H.-Y.: Speech Feature Extraction Using Independent Component Analysis. In: Proc. ICASP, Istanbul, Turkey, vol. 3(6), pp. 1631–1634 (2000)Google Scholar
  5. 5.
    Bell, A.J., Sejnowski, T.J.: An Information Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7(6), 1129–1159 (1995)CrossRefGoogle Scholar
  6. 6.
    Lee, T.-W., Lewicki, M.S.: The Generalized Gaussian Mixture Model Using ICA. In: International workshop on Independent Component Analysis (ICA 2000), Helsinki, Finland, pp. 239–244 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wei Kong
    • 1
  • Bin Yang
    • 2
  1. 1.Information Engineering CollegeShanghai Maritime UniversityShanghaiChina
  2. 2.Logistics Research CenterShanghai Maritime UniversityShanghaiChina

Personalised recommendations