Feature extraction approach to blind source separation

Juan K. Lin, David G. Grier, Jack D. Cowan

    Research output: Contribution to conferencePaper

    Abstract

    Local independent component analysis is formulated as a task involving the extraction of local geometric structure in the joint distribution. Because the geometrical structure of statistical independence is not well captured by statistical descriptions such as moments and cumulants, we use feature detection tools from image analysis to locate the local independent component coordinate system. The resulting approach to source separation can be implemented in real time using conventional image analysis hardware. The generality of this approach is demonstrated by blind source separation of multi-modal sources, and the pseudo-separation of three sources from two mixtures.

    Original languageEnglish (US)
    Pages398-405
    Number of pages8
    StatePublished - 1997
    EventProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97 - Amelia Island, FL, USA
    Duration: Sep 24 1997Sep 26 1997

    Other

    OtherProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97
    CityAmelia Island, FL, USA
    Period9/24/979/26/97

    ASJC Scopus subject areas

    • Signal Processing
    • Software
    • Electrical and Electronic Engineering

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  • Cite this

    Lin, J. K., Grier, D. G., & Cowan, J. D. (1997). Feature extraction approach to blind source separation. 398-405. Paper presented at Proceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97, Amelia Island, FL, USA, .