Neural Network Modeling of Speech and Music Signals

Time series prediction is one of the major applications of neural networks. Aftera short introduction into the basic theoretical foundations we argue that the iterated prediction of a dynamical system may be interpreted asa model of the system dynamics. By means of RBF neural networks we describe a modeling approach and extend it to be able to model instationary systems. As a practical test for the capabilities of the method we investigate the modeling of musical and speech signals and demonstrate that the model may be used for synthesis of musical and speech signals. 1 Introduction Since the formulation of the reconstruction theorem by Takens [10] it has been clear that a nonlinear predictor of a dynamical system may be directly derived from a systems time series. The method has been investigated extensively and with good success for the prediction oftime series of nonlinear systems.