Enhanced signal classification scheme using a selected information in the ambiguity domain
Dean Korošec, C. Doncarli · 2003
We present a new time-frequency classification procedure, based on the assumption that in decision problems the redundancy of two-dimensional time-frequency representations should be decreased by investigating only the most interesting parts of the time-frequency representation. Our implementation uses a generic time-frequency representation-the ambiguity function (AF). The classification problem involving several classes can be resolved by generalisation of the 'contrast' information measure between the mean AFs of all signal classes, or, as we propose, by creating a number of optimal class-to-class comparisons and combine their results as conditional likelihoods. Both classification approaches are demonstrated on a speech signal discrimination problem, for which the latter scheme yields superior results.