JSM 2004 - Toronto

Abstract #301912

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Activity Number: 428
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #301912
Title: Classification Using Time-frequency Representations
Author(s): Justin W. Davis*+
Companies: Baylor University
Address: One Bear Place #97328, Waco, TX, 76798,
Keywords: time-frequency representations ; discriminant analysis ; nonstationary ; time series ; wavelet packets ; pattern recognition
Abstract:

Techniques for discrimination and clustering of serial data have been employed in many different scientific areas. Applications include speech pattern recognition, the categorization of seismic records, and the classification of radar signals. Since many such time series exhibit nonstationary dynamics, a need exists for classification and clustering procedures that exploit temporal frequency shifts. Characterizing frequency dynamics is possible using a time-frequency representation (TFR). Various transforms can be used to obtain a TFR for a time series, and each method has its advantages and disadvantages. Several discriminants are proposed to be used in conjunction with TFRs to classify nonstationary series. The effectiveness of such methods are discussed, and an application of these methods to real data is given.

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