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Author McLachlan, G.; Majdak, P.; Reijniers, J.; Peremans, H. url  doi
openurl 
  Title Towards modelling active sound localisation based on Bayesian inference in a static environment Type A1 Journal article
  Year (down) 2021 Publication Acta Acustica Abbreviated Journal  
  Volume 5 Issue Pages 45  
  Keywords A1 Journal article; Engineering Management (ENM); Condensed Matter Theory (CMT)  
  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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Wos 000709050000001 Publication Date 2021-10-21  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Additional Links UA library record; WoS full record; WoS citing articles  
  Impact Factor Times cited Open Access OpenAccess  
  Notes Approved Most recent IF: NA  
  Call Number UA @ admin @ c:irua:182453 Serial 7035  
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