De-noising of Underwater Acoustic Signals Based on ICA Feature Extraction
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.
KeywordsBasis Vector Independent Component Analysis Independent Component Analysis Sparse Code Noisy Signal
- 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.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
- 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
- 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.Hyvärinen, A., Hoyer, P., Oja, E.: Image denoising by sparse code shrinkage, Intelligent Signal Processing. IEEE Press, Los Alamitos (2000)Google Scholar