Source separation in strong noisy mixtures: A study of wavelet de-noising pre-processing
This paper addresses the source separation in strong noisy mixtures by wavelet de-noising processing. Experiments include the cases of white/correlated Gaussian and non- Gaussian noise, which correspond to various applications. The performance of BSS/ICA algorithms after wavelet de- noising is quantitatively investigated, and points out the efficiency of the method.
WOS:000177510400421
2-s2.0-14844324646
2002
2
1681
1684
Swiss Federal Institute of Technol., Lausanne, Switzerland Cited By: 3; Export Date: 14 August 2006; Source: Scopus CODEN: IPROD Language of Original Document: English Correspondence Address: Paraschiv-Ionescu, A.; Swiss Federal Institute of Technol. Lausanne, Switzerland References: Akuzawa, T., New fast factorization method for multivariate optimization and its realization as ICA algorithm http://www.mns.brain.riken.go.jp/'akuzawa; Attias, H., Independent factor analysis (1999) Neural Computation, 11, pp. 803-851; Buckheit, J., Donoho, D.L., Wavelab and reproductible research (1994) Wavelets in Statistics, pp. 55-82, In A. Antoniadis and G. Oppenheim, editors; Cichocki, A., Douglas, S.C., Amari, S., Robust techniques for independent component analysis with noisy data (1998) Neurocomputing, 22, pp. 113-129; Donoho, D.L., Johnstone, I.M., Adapting to unknown smoothness via wavelet shrinkage (1995) J. Am. Statist. Ass., 90, pp. 1200-1244; Donoho, D.L., Yu, T.P.Y., Nonlinear wavelet transforms based on median interpolation http://www- stat.stanford.edu/~donoho/Reports/; Hyvarinen, A., Karhunen, J., Oja, E., (2001) Independent Component Analysis, John Wiley & Sons; Mallat, S.G., A theory of multiresolution signal decomposition: The wavelet representation (1989) IEEE Trans. Pattn. Anal. Mach. Intell., 11, pp. 674-693. Sponsors: IEEE
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