A generalized musical-tone generator with application to sound compression and synthesis

Amusical-tone generator based on physical modeling of the sound production mechanisms is presented. To the purpose of making this scheme general for a wide class of musical instruments, the nonlinear part of the tone-generator is modeled by a neural network. The system learns its parameters and the nonlinearity shape by means of nonlinear identication procedures based on waveform or spectral matching. Two possible applications of this model are discussed: sound compression can be obtained when considering the system as a nonlinear predictor, while sound synthesis can be obtained by adding control inputs to the network and by training the system to respond as desired.