Author:
McLachlan Glen,Majdak Piotr,Reijniers Jonas,Peremans Herbert
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 aspect is the consideration of dynamic localisation cues obtained through self-motion. Here we provide a review of the recent developments in modelling dynamic sound localisation with a particular focus on Bayesian inference. Further, we describe a theoretical Bayesian framework capable to model dynamic and active listening situations in humans in a static auditory environment. In order to demonstrate its potential in future implementations, we provide results from two examples of simplified versions of that framework.
Funder
Fonds Wetenschappelijk Onderzoek
European Union
Subject
Electrical and Electronic Engineering,Speech and Hearing,Computer Science Applications,Acoustics and Ultrasonics
Cited by
12 articles.
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