Title :
Automatic classification of positive time-frequency distributions
Author :
Pitton, James W. ; Atlas, Les E.
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
Abstract :
A method of performing automatic classification of positive time-frequency distributions is presented. These distributions are computed via constrained optimization, minimizing the cross-entropy of the distribution subject to a set of constraints. An algorithm for clustering using cross-entropy as the distance measure between vectors was derived by Shore and Gray (see IEEE Trans. PAMI, vol.4, no.1, p.11-17). We apply this method to the time-frequency case, and derive an efficient classification scheme. An advantage of this method is that the time-frequency distributions of the data to be classified do not need to be directly computed; thus, the method can be applied to real-time classification
Keywords :
entropy; optimisation; pattern classification; signal representation; signal synthesis; spectral analysis; statistical analysis; time-frequency analysis; automatic classification; clustering algorithm; constrained optimization; cross-entropy minimisation; distance measure; pattern classification; positive time-frequency distributions; real-time classification; signal representation; signal synthesis; spectral analysis; speech recognition; vectors; Clustering algorithms; Constraint optimization; Distributed computing; Interactive systems; Laboratories; Signal resolution; Signal synthesis; Spectrogram; Speech; Time frequency analysis;
Conference_Titel :
Time-Frequency and Time-Scale Analysis, 1994., Proceedings of the IEEE-SP International Symposium on
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-7803-2127-8
DOI :
10.1109/TFSA.1994.467336
