Improved optimization of time-frequency-based signal classifiers

Time-frequency representations (TFRs) are efficient tools for nonstationary signal classification. However, the choice of the TFR and of the distance measure employed is critical when no prior information other than a learnin g set of limited size is available. In this letter we propose to jointly op timize the TFR and distance measure by minimizing the (estimated) probabili ty of classification error The resulting optimized classification method is applied to multicomponent chirp signals and real speech records (speaker r ecognition). Extensive simulations show the substantial improvement of clas sification performance obtained with our optimization method.