Optimal Kernels of Time-frequency Representations for Signal Classification
نویسندگان
چکیده
ABSTRACT The use of distances between Time-Frequency Representations (TFRs) has recently led to a time-frequency formulation of the problem of non-stationary signals classification. In this paper, 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.
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