Modeling loudspeakers based on cascading lumped parameter models with neural networks

摘要

In one embodiment of the present invention, a loudspeaker modeling subsystem configures a neural lumped parameter loudspeaker (NeLP) model to represent the behavior of a loudspeaker. The NeLP model is implemented as a cascaded combination of a lumped parameter model (LPM) and a neural network (NN) model. To configure the model, the loudspeaker modeling subsystem first estimates values for the parameters used in the LPM. The loudspeaker modeling subsystem then “fixes” these parameters and trains the NN model to act on a predicted output pressure that is generated via the LPM. More specifically, the loudspeaker modeling subsystem configures the NN to modify the predicted output pressure to minimize the error between the predicted output pressure and a measured loudspeaker output pressure. Notably, by strategically fusing the LPM and the NN model, the NeLP model leverages the strengths and mitigates the weaknesses typically associated with conventional loudspeaker modeling techniques.