Neuronal networks for pattern recognition

To make efficient practical use of the attractive properties of standard neural network models it seems reasonable to combine a recognition network with appropiate conventional preprocessing. In this contribution we describe the current research at IPA concerning such a hybrid approach to acoustic pattern recognition. the inputs of the recongition network are feature vectors consisting of local frequency characteristics extracted from the Wigner representation of the patterns. Simulations show that the system is capable to recognize individually learned objects in a scene.