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Hear Res. 2018 Mar;360:92-106. doi: 10.1016/j.heares.2017.11.010. Epub 2017 Nov 28.

A framework for testing and comparing binaural models.

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National Centre for Audiology, Western University, London, ON, Canada. Electronic address:
Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom.
Institut für Schallforschung, Österreichische Akademie der Wissenschaften, Wien, Austria.
Carnegie Mellon University, Pittsburgh, PA, United States.
Ear Institute, University College London, London, United Kingdom.
Medizinische Physik, Universität Oldenburg, Oldenburg, Germany.
Michigan State University, East Lansing, MI, United States.


Auditory research has a rich history of combining experimental evidence with computational simulations of auditory processing in order to deepen our theoretical understanding of how sound is processed in the ears and in the brain. Despite significant progress in the amount of detail and breadth covered by auditory models, for many components of the auditory pathway there are still different model approaches that are often not equivalent but rather in conflict with each other. Similarly, some experimental studies yield conflicting results which has led to controversies. This can be best resolved by a systematic comparison of multiple experimental data sets and model approaches. Binaural processing is a prominent example of how the development of quantitative theories can advance our understanding of the phenomena, but there remain several unresolved questions for which competing model approaches exist. This article discusses a number of current unresolved or disputed issues in binaural modelling, as well as some of the significant challenges in comparing binaural models with each other and with the experimental data. We introduce an auditory model framework, which we believe can become a useful infrastructure for resolving some of the current controversies. It operates models over the same paradigms that are used experimentally. The core of the proposed framework is an interface that connects three components irrespective of their underlying programming language: The experiment software, an auditory pathway model, and task-dependent decision stages called artificial observers that provide the same output format as the test subject.


Auditory models; Binaural processing; Interaural time differences; Psychoacoustics; Reproducible research

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