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Blind Source Separation Via ICA: The Team and Thanks

Module by: Angela Qian, John Steinbauer, Akshay Dayal, Mark Eastaway. E-mail the authors

Summary: 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 Bioelectromagnetism2004, 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.

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A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

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What are tags? tag icon

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