Model-based Bayesian direction of arrival analysis for sound sources using a spherical microphone array

Abstract

In many room acoustics and noise control applications, it is often challenging to identify the directions of arrivals (DoAs) of incoming sound sources. This work seeks to solve this problem reliably by beamforming, or spatially filtering, incoming sound data with a spherical microphone array coupled with a probabilistic model selection method. When estimating the DoA, the signal under consideration may contain one or multiple concurrent sound sources originating from different directions. This leads to a two-tiered challenge of first identifying the correct number of sources, followed by determining the directional information of each source. To this end, a probabilistic method of model-based Bayesian analysis is leveraged. This entails generating analytic models of the experimental data, individually defined by a specific number of sound sources and their locations in physical space, and comparing each model to the measured data. Through this process, the number of sources is first estimated, and then the DoA information of those sources is extracted from the model that most closely correlates to the experimental data. This thesis will present the analytic models, the Bayesian formulation, and preliminary results to demonstrate the potential usefulness of this model-based Bayesian analysis for complex acoustic environments with potentially multiple concurrent sound sources.