Abstract
Independent Component Analysis (ICA) (Comon, 1994; Lee, 1998; Karhunen et al,1997; Haykin, 1998) is an unsupervised technique, which tries to represent the data in terms of statistically independent variables. ICA and the related blind source separation (BSS) and application topics both in unsupervised neural learning and statistical signal processing.
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References
Bell, A. J. and T. J. Sejnowski (1995). An information-maximisation approach to blind separation and blind deconvolution, Neural Computation, 7 (6), 1004–1034.
Cardoso J.F. (1989) Source Separation Using Higher Order Moments, IEEE Transactions on Signal Processing, Vol. No. pp. 2109–2112
Cardoso JF and Comon P (1996). Independent component analysis, a survey of some algebraic methods. In Proc. ISCAS’96, vol. 2, pp. 93–96.
Chan, D. C. B. (1997). Blind Signal Processing, PhD thesis, Signal Processing and Communications Laboratory Department of Engineering, University of Cambridge, UK.
Comon P. (1994), Independent component analysis-a new concept?, signal processing, vol. 36, pp. 287–314.
Haykin S. (1998), Neural networks- A comprehensive Foundation, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall.
Karhunen J., Oja E., etc. (1997), A class of neural networks for independent component analysis, IEEE Trans. On Neural Networks, vol. 8, pp. 486–504.
Lee T. W. (1998), Independent component analysis-theory and application, Dordrecht, The Netherlands: Kluwer.
Li Y., Powers D. and Wen P. (2001), Separation and Deconvolution of Speech Using Recurrent Neural Networks, pp. 1303–1309, Vol. III, Proceedings of the International Conference on Artificial Intelligence (IC-AI’01), June 25–28, 2001, Las Vegas, Nevada, USA.
Lin J., Grier D. and Cowan J. (1997), Faithful representation of separable distributions, Neural Computation, Vol. 9, pp. 1305–1320.
Mansour A and Jutten C (1995) Fourth Order Criteria for Blind Sources Separation. IEEE Transactions on Signal Processing, Vol. 43, No. 8 August 1995. pp. 2022–2025.
Mansour A. and Ohnishi N. (1999), Multichannel Blind Separation of Sources Algorithm Based on Cross- Cumulant and the Levenberg-Marquardt Method, Vol. 47, No. 11, November, pp. 3172–3179.
Nguyen Thi H.L. and Jutten C. (1995) Blind Source Separation for Convolutive Mixtures, Signal Processing Vol. 45, pp. 209–229.
Schobben, D. (1999), Efficient adaptive multi-channel concepts in acoustics: Blind signal separation and echo cancellation, PhD thesis.
Vicente Zarzoso, Asoke K. Nandi (1999), Blind Separation of Independent sources for Virtually Any Source Probability Density Function, IEEE transactions on signal processing, Vol. 47, No. 9, 2419–2432.
Westner, A. G. (1999). Object based audio capture: Separating acoustically mixed sounds. Master’s thesis, MIT.
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Li, Y., Powers, D.M.W. (2002). Speech Separation Based on Higher Order Statistics Using Recurrent Neural Networks. In: Abraham, A., Köppen, M. (eds) Hybrid Information Systems. Advances in Soft Computing, vol 14. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1782-9_5
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DOI: https://doi.org/10.1007/978-3-7908-1782-9_5
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