De-noising of Underwater Acoustic Signals Based on ICA Feature Extraction

  • Kong Wei
  • Yang Bin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

As an efficient sparse coding and feature extraction method, independent component analysis (ICA) has been widely used in speech signal processing. In this paper, ICA method is studied in extracting low frequency features of underwater acoustic signals. The generalized Gaussian model (GGM) is introduced as the p.d.f. estimator in ICA to extract the basis vectors. It is demonstrated that the ICA features of ship radiated signals are localized both in time and frequency domain. Based on the ICA features, an extended de-noising method is proposed for underwater acoustic signals which can extract the basis vectors directly from the noisy observation. The de-noising experiments of underwater acoustic signals show that the proposed method offers an efficient approach for detecting weak underwater acoustic signals from noise environment.

Keywords

Basis Vector Independent Component Analysis Independent Component Analysis Sparse Code Noisy Signal 
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.

References

  1. 1.
    Lee, T.-W., Jang, G.-J.: The Statistical Structures of Male and Female Speech Signals. In: Proc. ICASSP, Salt Lack City, Utah (May 2001)Google Scholar
  2. 2.
    Lee, J.-H., Jung, H.-Y.: Speech Feature Extraction Using Independent Component Analysis. In: Proc. ICASP, Istanbul, Turkey, vol. 3, pp. 1631–1634 (June 2000)Google Scholar
  3. 3.
    Anthony, J., Bell, T.J.: Learning the Higher-order structure of a nature sound. Network: Computation in Neural System 7, 261–266 (1996)MATHCrossRefGoogle Scholar
  4. 4.
    Jang, G.-J., Lee, T.-w.: Learning statistically efficient features for speaker recognition. Neurocomputing 49, 329–348 (2002)CrossRefGoogle Scholar
  5. 5.
    Miller, J.H., Thomas, J.B.: Detectors for Discrete-Time Signals in Non-Gaussian noise. IEEE Transactions on Information Theory IT-18(2), 241–250 (1972)MATHCrossRefGoogle 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 (June 2000)Google Scholar
  7. 7.
    Hyvärinen, A.: Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation. Neural Computation 11(7), 1739–1768 (1999)CrossRefGoogle Scholar
  8. 8.
    Hyvärinen, A., Hoyer, P., Oja, E.: Sparse code shrinkage: Denoising by nonlinear maximum likelihood estimation. In: Advances in Neural Information Processing System 11, NIPS 1998 (1999)Google Scholar
  9. 9.
    Hyvärinen, A., Hoyer, P., Oja, E.: Image denoising by sparse code shrinkage, Intelligent Signal Processing. IEEE Press, Los Alamitos (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kong Wei
    • 1
  • Yang Bin
    • 1
  1. 1.Information Engineering CollegeShanghai Maritime UniversityShanghaiChina

Personalised recommendations