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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  . We apply this method to the time-frequency case, and derive an efficient classification scheme. An advantage of this method isdoi:10.1002/9781119198987.ch11 fatcat:ktqn7scyljh2vl3pyh3txxxf4a