Abstract
Different computational models have been developed to study the interaural time difference (ITD) perception. However, only few have used a physiologically inspired architecture to study ITD discrimination. Furthermore, they do not include aspects of hearing impairment. In this work, a framework was developed to predict ITD thresholds in listeners with normal and impaired hearing. It combines the physiologically inspired model of the auditory periphery proposed by Zilany, Bruce, Nelson, and Carney [(2009). J. Acoust. Soc. Am. 126(5), 2390-2412] as a front end with a coincidence detection stage and a neurometric decision device as a back end. It was validated by comparing its predictions against behavioral data for narrowband stimuli from literature. The framework is able to model ITD discrimination of normal-hearing and hearing-impaired listeners at a group level. Additionally, it was used to explore the effect of different proportions of outer- and inner-hair cell impairment on ITD discrimination.
Highlights
The best known theory of how we process interaural time differences (ITDs) was proposed by Jeffress (1948)
Where a is the bottom limit of d0, b is the top limit of d0, c is the ITDexp value that yields a d0 value corresponding to 50% of the range of the fitted curve, and d is the slope
We presented a physiologically based modelling framework capable of predicting ITD thresholds for NH and HI listeners
Summary
The best known theory of how we process interaural time differences (ITDs) was proposed by Jeffress (1948). Studying HI using a physiologically based computational approach (Durlach et al, 1981; Moore, 1996) would allow us to systematically investigate the mechanisms that are detrimental to the (binaural) hearing system and the effect of specific aspects of HI in listeners’ performance in different tasks without the confounds of behavioral experiments. We used our framework to study ITD discrimination by predicting its thresholds (i.e., just noticeable difference) in both NH and HI listeners It combines the physiologically inspired front end proposed by Zilany et al (2014, 2009) with a coincidence detection stage and a neurometric-based decision device as a back end. We used it to explore the effect of different proportions of outer- and inner-hair cell (OHC, IHC) impairment on ITD discrimination
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