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- Elec 301 projects fall 2007
- Blind source separation via ica
- Blind source separation via ica
This module lists our team members as well as cites sources we used during our implementation of a blind source separation system using fast ICA.
Blind source separation via ica
The team and thanks
The team
Thanks and recognition
The team would also like to thank Professor Richard Barniuk and Mark Davenport, who helped jumpstart us on our project. We also would like to thank Michael Dye and the Rice University ERC for providing the microphone hardware we used to implement our project.
References
- Gävert, H., Hurri, J., Särelä, J., and Hyvärinen, A. (2005).
The FastICA Package for MATLAB . Free Software Foundation.
- Hyvarinen, A. and Erkki, O.(2000).
Independent Component Analysis: Algorithms and Applications . Neural Networks, 13(4-5): 411-430.
- Cichocki, A. (2004). Blind Signal Processing Methods for AnalyzingMultichannel Brain Signals. International Journal of Bioelectromagnetism
2004, Vol. 6, No. 1 .
- Jourjine, A., Rickard, S., Yilmaz, O. (2000). Blind Separation of Disjoint Orthogonal Signals: Demixing N Sources from 2 Mixtures. IEEE.
- Fiori, S. (1999). Entrophy Optimization by the PFANN Network: Application to Blind Source Separation. Network: Cornput. Neural Syst. 10 171-186.
- Meyer-Base, A., Gruber, P., Theis, F., and Foo, S. (2005). Blind Source Separation Based on Self-Organizing Neural Network. Science Direct.
Source:
OpenStax, Elec 301 projects fall 2007. OpenStax CNX. Dec 22, 2007 Download for free at http://cnx.org/content/col10503/1.1
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