Binaural sound source localisation using a Bayesian-network-based blackboard system and hypothesis-driven feedback

An essential aspect of Auditory Scene Analysis is the localisation of sound sources in relation to the position of the listener in the surrounding environment. The human auditory system is capable of precisely locating and separating different sound sources, even in noisy and reverberant environments, whereas mimicking this ability by computational means is still a challenging task. In this work, we investigate a Bayesian-network-based approach in the context of binaural sound source localisation. We extend existing solutions towards a Bayesian network based blackboard system that includes expert knowledge inspired by insights into the human auditory system. In order to improve estimation of source positions and reduce uncertainty caused by front-back ambiguities, hypothesis-driven feedback is used. This is accomplished by triggering head movements based on inference results provided by the Bayesian network. We evaluate the performance of our approach in comparison to existing solutions in a sound-source localisation task within a virtual acoustic environment.