Abstract
A maximum likelihood blind source separation algorithm is developed. The temporal dependencies are explained by assuming that each source is an AR process and the distribution of the associated i.i.d. innovations process is described using a Mixture of Gaussians (MOG). Unlike most maximum likelihood methods the proposed algorithm takes into account both spatial and temporal information, optimization is performed using the Expectation-Maximization method, and the source model is learned along with the demixing parameters.
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Keywords
- Independent Component Analysis
- Blind Source Separation
- Separation Performance
- Target Distribution
- Signal Proc
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References
Van Gerven, S., Van Compernolle, D.: Signal separation by symmetric adaptive decorrelation: stability, convergence, and uniqueness. IEEE Trans. on Signal Proc. 43, 1602–1612 (1995)
Weinstein, E., Feder, M., Oppenheim, A.: Multi-channel signal separation by decorrelation. IEEE Trans. on Speech and Audio Proc. 1, 405–413 (1993)
Wu, H.C., Principe, J.C.: A unifying criterion for blind source separation and decorrelation: simultaneous diagonalization of correlation matrices. Neural Networks for Signal Proc., 496–505 (1997)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)
Muller, K.R., Philips, P., Ziehe, A., Jade, T.D.: Combining higher-order statistics and temporal information for blind source separation (with noise). In: Intl. Workshop on Independent Component Analysis and Signal Separation, pp. 87–92 (1999)
Hild II, K.E., Erdogmus, D., Principe, J.C.: An Analysis of Entropy Estimators for Blind Source Separation. Signal Processing 86, 182–194 (2006)
Moulines, E., Cardoso, J.F., Gassiat, E.: Maximum Likelihood for blind source separation and deconvolution of noisy signals using mixture models. In: Intl. Conf. on Acoustics, Speech, and Signal Processing, vol. 5, pp. 3617–3620 (1997)
Pham, D.T., Garat, P.: Blind separation of mixture of independent sources through a quasi-maximum likelihood approach. IEEE Trans. on Signal Proc. 45, 1712–1725 (1997)
Pearlmutter, B.A., Parra, L.C.: Maximum likelihood blind source separation: A context-sensitive generalization of ICA. Advances in Neural Information Proc. Systems 9, 613–619 (1996)
Amari, S.: Neural learning in structured parameter spaces - Natural Riemannian gradient. Advances in Neural Information Proc. Systems 9, 127–133 (1996)
Cardoso, J.F., Laheld, B.H.: Equivariant adaptive source separation. IEEE Trans. on Signal Proc. 44, 3017–3030 (1996)
Cardoso, J.F., Souloumiac, A.: Blind beamforming for non-Gaussian signals. IEE Proceedings F-140, 362–370 (1993)
Rabiner, L.R., Schafer, R.W.: Digital Processing of Speech Signals (1978)
Hosseini, S., Jutten, C., Pham, D.T.: Markovian source separation. IEEE Trans. on Signal Proc. 51, 3009–3019 (2003)
Amari, S.I., Cardoso, J.F.: Blind source separation-Semiparameteric statistical approach. IEEE Trans. on Signal Proc. 45, 2692–2700 (1997)
Cruces-Alvarez, S.A., Cichoki, A., Amari, S.I.: On a new blind signal extraction algorithm: Different criteria and stability analysis. IEEE Signal Proc. Letters 9, 233–236 (2002)
Cardoso, J.F.: Infomax and maximum likelihood for blind source separation. IEEE Signal Proc. Letters 4, 112–114 (1997)
Attias, H.: Independent factor analysis. Neural Computation 11, 803–851 (1999)
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© 2006 Springer-Verlag Berlin Heidelberg
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Hild, K.E., Attias, H.T., Nagarajan, S.S. (2006). An EM Method for Spatio-temporal Blind Source Separation Using an AR-MOG Source Model. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_13
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DOI: https://doi.org/10.1007/11679363_13
Publisher Name: Springer, Berlin, Heidelberg
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