Nonlinear Model and its Inverse of an Audio System
This work addresses improving audio system modeling for audio engineering applications, but it is incremental as it applies existing methods to a specific domain.
The thesis tackled modeling loudspeaker nonlinearities using a third-order Volterra series and its inverse, trained with a Normalized Least Mean Square algorithm, resulting in a decrease in Mean Squared Error compared to a linear model, though performance varied with test signals and model parameters.
This computer science master thesis aims at modelling the nonlinearities of a loudspeaker. A piecewise linear approximation is initially explored and then we present a nonlinear Volterra model to simulate the behavior of the system. The general theory of continuous and discrete Volterra series is summarised. A Normalized Least Mean Square algorithm is used to determine the Volterra series to third order. We also present as inverted system which is trained with the same algorithm. Training data for the models were collected measuring a physical speaker using a laser interferometer. Results indicate a decrease in Mean Squared Error compared to the linear model with a dependency on the particular test signal, the order and the parameters of the model.