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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wei, K., Bin, Y. (2005). De-noising of Underwater Acoustic Signals Based on ICA Feature Extraction. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_94
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DOI: https://doi.org/10.1007/11578079_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)
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