Towards modelling active sound localisation based on Bayesian inference in a static environment

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 a...

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Autores principales:	McLachlan Glen, Majdak Piotr, Reijniers Jonas, Peremans Herbert
Formato:	article
Lenguaje:	EN
Publicado:	EDP Sciences 2021
Materias:	sound localisation active listening dynamic cues bayes models Acoustics in engineering. Acoustical engineering TA365-367 Acoustics. Sound QC221-246
Acceso en línea:	https://doaj.org/article/0f82d0c09bbe4968b2c4e38340747c2c
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id	oai:doaj.org-article:0f82d0c09bbe4968b2c4e38340747c2c
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spelling	oai:doaj.org-article:0f82d0c09bbe4968b2c4e38340747c2c2021-12-02T17:10:37ZTowards modelling active sound localisation based on Bayesian inference in a static environment2681-461710.1051/aacus/2021039https://doaj.org/article/0f82d0c09bbe4968b2c4e38340747c2c2021-01-01T00:00:00Zhttps://acta-acustica.edpsciences.org/articles/aacus/full_html/2021/01/aacus210042/aacus210042.htmlhttps://doaj.org/toc/2681-4617Over 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.McLachlan GlenMajdak PiotrReijniers JonasPeremans HerbertEDP Sciencesarticlesound localisationactive listeningdynamic cuesbayesmodelsAcoustics in engineering. Acoustical engineeringTA365-367Acoustics. SoundQC221-246ENActa Acustica, Vol 5, p 45 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sound localisation active listening dynamic cues bayes models Acoustics in engineering. Acoustical engineering TA365-367 Acoustics. Sound QC221-246
spellingShingle	sound localisation active listening dynamic cues bayes models Acoustics in engineering. Acoustical engineering TA365-367 Acoustics. Sound QC221-246 McLachlan Glen Majdak Piotr Reijniers Jonas Peremans Herbert Towards modelling active sound localisation based on Bayesian inference in a static environment
description	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.
format	article
author	McLachlan Glen Majdak Piotr Reijniers Jonas Peremans Herbert
author_facet	McLachlan Glen Majdak Piotr Reijniers Jonas Peremans Herbert
author_sort	McLachlan Glen
title	Towards modelling active sound localisation based on Bayesian inference in a static environment
title_short	Towards modelling active sound localisation based on Bayesian inference in a static environment
title_full	Towards modelling active sound localisation based on Bayesian inference in a static environment
title_fullStr	Towards modelling active sound localisation based on Bayesian inference in a static environment
title_full_unstemmed	Towards modelling active sound localisation based on Bayesian inference in a static environment
title_sort	towards modelling active sound localisation based on bayesian inference in a static environment
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/0f82d0c09bbe4968b2c4e38340747c2c
work_keys_str_mv	AT mclachlanglen towardsmodellingactivesoundlocalisationbasedonbayesianinferenceinastaticenvironment AT majdakpiotr towardsmodellingactivesoundlocalisationbasedonbayesianinferenceinastaticenvironment AT reijniersjonas towardsmodellingactivesoundlocalisationbasedonbayesianinferenceinastaticenvironment AT peremansherbert towardsmodellingactivesoundlocalisationbasedonbayesianinferenceinastaticenvironment
_version_	1718381480748515328

Towards modelling active sound localisation based on Bayesian inference in a static environment

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