System identifications for interior acoustic problems using the probabilistic approach

Abstract

This study addresses problems of system identification for room acoustics. Two problems are investigated: (1) identifying leakages on a wall surface of a room that is subject to an external noise, and (2) distinguishing among the interior pressures that are induced from independent sound sources within a room. A time-domain Bayesian probabilistic framework that incorporates model class selection is developed for the system identification processes of the two problems. A model class selection index is defined that is used to evaluate the accuracy of different acoustic models and to identify the best model. The optimal values that are assigned to the unknown parameters of the acoustic models are identified from the peak values of the corresponding probability density functions. A series of parametric studies is conducted to investigate the effects of different parameters on the accuracy of the system identifications for the two problems. Full-scale experiments are carried out, and the results are used to verify the predictions that are made based on the theoretical simulations. In general, the experimental results agree well with the theoretical predictions.