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Proceedings Paper

Time-frequency-based classification
Author(s): Berkant Tacer; Patrick J. Loughlin
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Paper Abstract

We propose a time-frequency based pattern classification method which utilizes the joint moments of time-frequency distributions (TFDs) for features. The method is applied to a biomedical data set, and compared to a template matching scheme and to methods utilizing only temporal moments or spectral moments. Our results show that a classification algorithm which utilizes joint time-frequency information, as quantified by the joint moments of the TFD, can potentially improve performance over time or frequency-based methods alone, for classification of nonstationary time series.

Paper Details

Date Published: 22 October 1996
PDF: 7 pages
Proc. SPIE 2846, Advanced Signal Processing Algorithms, Architectures, and Implementations VI, (22 October 1996); doi: 10.1117/12.255432
Show Author Affiliations
Berkant Tacer, Univ. of Pittsburgh (United States)
Patrick J. Loughlin, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 2846:
Advanced Signal Processing Algorithms, Architectures, and Implementations VI
Franklin T. Luk, Editor(s)

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