Non stationary signals classification using time-frequency distributions

The paper deals with a comparison between different non-parametric classification methods of non stationary signals. The first ones, consider the time frequency representation (TFR) of the signal as the code itself and the decision is taken following the value of a dissimilarity index between the TFRs. In the other methods, the authors compute the instantaneous log-deviation between the (positive) TFR of the signal to be classified, and the TFR of each cluster. The classification results of each method (misclassification rate versus the cardinal of the learning set) are presented, and the influence of the choice of the TFR is studied