Learning pseudo-physical models for sound synthesis and transformation

Synthesis by physical models is a sound synthesis technique which has recently become popular due to sound duality and expressiveness of control. We propose a rather general structure based on an interaction scheme where the nonlinear component is modeled by radial basis function (RBF) networks. This leads to a system which has the ability to learn the shape of the nonlinearity in order to reproduce a target sound. From the waveform data it is possible to deduce a training set for off-line learning techniques, and the parameters of the RBF network are computed by iterated selection of the radial units. In this work we first consider memoryless nonlinear exciters. After then, dynamic exciters are simulated by adopting a nonlinear ARMA model. Once the system has converged to a well behaved instrument model, it is possible to control sound features, such as pitch, by modifying the physically-informed parameters in an intuitive way.