Bayesian inference for acoustic impedance boundaries in room-acoustic finite difference time-domain modeling

摘要

In room acoustics, the finite difference time-domain approach is increasingly being applied to model wave propagation in spaces with complex geometries. For realistic simulation, implementation of frequency-dependent, impedance boundary conditions, is necessary. This paper will demonstrate that the modeling and implementation of acoustic impedance boundaries within the finite difference time-domain approach represents tasks which can be solved by two levels of Bayesian inference. The impedance function is expressed as a parametric model with coefficients of finite order, and the order of the model is directly connected to the accuracy of the calculation, computational expense, and memory requirements. The implicit Occam's razor for boundary impedance model selection, followed by coefficient estimation within the Bayesian framework, can be applied for optimizing computational expense and accuracy achieved in room-acoustic finite difference time-domain modeling.