Optimal kernels of time-frequency representations for signal classification

The use of distances between time-frequency representations (TFRs) has led to a time-frequency formulation of the problem of non-stationary signals classification. We propose a new method based upon the optimization of the TFR, remaining in the Cohen's group. We show that a radially Gaussian kernel and a Fisher-like contrast criterion provide improved classification results.