Abstract
This paper presents a PDF-matched modification to Stone’s measure of predictability. The modified measure of predictability is a measure of non-gaussianity too. It is an extent of signal predictability by two different prediction terms. One prediction term is based on a normal gaussian PDF assumption for signal. In contrast, the other one is based on a unit variance supergaussian PDF assumption for signal. By contrastive deployment of the above prediction terms, the modified measure of predictability enables BSS to follow a high kurtosis PDF assumption for signals. As an advantage, not only signals with maximized predictability are recovered, but also with increased non-gaussianity too. Deploying the modified measure of predictability concludes more independent recovered signals. The dominance of BSS based on the modified measure to the previous one has been demonstrated by many tests performed over mixtures of realistic audio signals (music and speech) and over mixtures of gray-scale images.
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References
Haykin, S.: Unsupervised Adaptive Filtering. Blind Source Separation, vol. 1. John Wiley & Sons, New York (2000)
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing. John Wiley & Sons, New York (2000)
Friedman, J.: Exploratory Projection Pursuit. Journal of the American Statistical Association 82, 249–266 (1987)
DeGroot, M.H.: Probability and Statistics. Addison-Wesley, Reading (1986)
Jutten, C., Herault, J.: Independent Component Analysis versus PCA. In: Proc. EUSIPCO, pp. 643–646. IEEE Press, New York (1988)
Bell, A.J., Sejnowski, T.J.: An Information-maximization Approach to Blind Separation and Blind Deconvolution. J. Neural computation 7, 1129–1159 (1995)
Stone, J.V.: Blind Source Separation using Temporal Predictability. Neurocomputing 13, 1559–1574 (2001)
Stone, J.V.: Independent Component Analysis: A Tutorial Introduction. A Bradford Book, London (2004)
Stone, J.V.: Blind Deconvolution using Temporal Predictability. Neurocomputing 49, 79–86 (2002)
Martinez, D., Bray, A.: Nonlinear blind source separation using kernels. IEEE Trans. Neural Networks 14, 228–335 (2003)
Khosravy, M., Asharif, M., Yamashita, K.: A Probabilistic Short-length Linear Predictability Approach to Blind Source Separation. In: 23rd International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC 2008), Yamaguchi, Japan, pp. 381–384 (2008)
Khosravy, M., Asharif, M., Yamashita, K.: An Efficient ICA Based Approach to Multiuser Detection in MIMO OFDM Systems. In: Plass, S., Dammann, A., Kaiser, S., Fazel, K. (eds.) Multi-Carrier Systems and Solutions MCSS 2009. LNEE. Springer, Heidelberg (2009)
Xie, S., He, Z., Fu, Y.: A Note on Stone’s Conjecture of Blind Signal sSeparation. Neural Computation 17, 321–330 (2005)
Borga, M., Landelius, T., Knutsson, H.: A Unified Approach to PCA, PLS, MLR and CCA. Report LiTH-ISY-R-1992, ISY, SE-581 83 Linkoping, Sweden (1997)
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Khosravy, M., Alsharif, M.R., Yamashita, K. (2009). A PDF-Matched Modification to Stone’s Measure of Predictability for Blind Source Separation. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_26
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DOI: https://doi.org/10.1007/978-3-642-01507-6_26
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