Automatic classification of positive time-frequency distributions
James W. Pitton, Les Atlas · 2002
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.>