Modelling 3D sound localisation with head movements based on Bayesian inference
* Presenting author
Abstract:
Over the decades, Bayesian statistical inference has become a staple technique for modelling human multisensory perception. Many studies have successfully shown how sensory and prior information can be combined to optimally interpret our environment. Because of the multiple sound localisation cues available in the binaural signal, sound localisation models based on Bayesian inference are a promising way of explaining behavioural human data. An interesting application is the integration of dynamic localisation cues, which are obtained through source or self-motion. The auditory system uses dynamic cues to complement the well-documented monaural and binaural static localisation cues and improve localisation accuracy. Here we provide a review of the recent developments in dynamic sound localisation modelling, with a particular focus on the prominent role of Bayesian inference in these processes. Finally, a theoretical framework is proposed for dynamic listening using Bayesian inference.