Volume 5, 2021
Topical Issue - Auditory models: from binaural processing to multimodal cognition
|Number of page(s)||16|
|Published online||20 October 2021|
Towards modelling active sound localisation based on Bayesian inference in a static environment
Department of Engineering Management, University of Antwerp, 2000 Antwerp, Belgium
2 Acoustics Research Institute, Austrian Academy of Sciences, 1040 Vienna, Austria
* Corresponding author: email@example.com
Accepted: 20 September 2021
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.
Key words: Sound localisation / Active listening / Dynamic cues / Bayes / Models
© G. McLachlan et al., Published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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