Computing deblurred time-frequency distributions using artificial neural networks

This paper presents an effective correlation vectored taxonomy algorithm to compute highly concentrated time-frequency distributions (TFDs) using localized neural networks (LNNs). Spectrograms and pre-processed Wigner-Ville distributions of known signals are vectorized and clustered as per the elbow criterion to constitute the training data for multiple artificial neural networks. The best trained networks become part of the LNNs. Test TFDs of unknown signals are then processed through the algorithm and presented to the LNNs. Experimental results demonstrate that appropriately vectored and clustered data once processed through the LNNs produce high resolution TFDs. Examples are presented to show the effectiveness of the proposed algorithm via analysis based on entropy and visual interpretation.