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)


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.


Basis Vector Independent Component Analysis Independent Component Analysis Sparse Code Noisy Signal 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

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

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