Neural Network Solution for Compesating Distortions of Time Frequency Representations

A Neural network (NN) based approach to obtain energy concentration along instantaneous frequencies (IFs) of the individual components present in the signal, is proposed. Blurry spectrograms and highly concentrated Wigner distributions (WDs) of various signals constitute the training set. The input data is grouped according to the underlying feature present in time frequency representation (TFR) image to have better generalization ability of the trained NN. Blurry TFRs of multi component signals are then given as test cases to the trained NN. Effectiveness of the approach is established by comparing the information content in each input and resultant TFR.