Repo to contain full source code
The project originates from the master thesis Simulating perceptual tasks with deep neural networks to improve diagnostics of hearing impairment (full text).
Measuring pure-tone thresholds is the gold standard clinical tool for assessing the health of the auditory system. However, several studies on both animals and humans show impairment and hearing deficits that are not reflected by the absolute threshold, leading to the term "hidden hearing loss (HHL)". Several electro-physiological measurements have been proposed as potential biomarkers for the assessment of HHL in humans. However, electro-physiological measurements require long recording times and the use of advanced equipment, presenting a challenge in their clinical applicability. The need for a behavioural test is therefore of high interest. Such tests have been suggested based on a heuristic interpretation of the effect of HHL on perception. The search for a behavioural test sensitive to HHL might lead to a non-optimal trial-and-error strategy. Using a state-of-art model of the auditory nerve (AN) paired with a deep neural network (DNN) model, we investigated whether a gap detection task could be used as a method for detecting cochlear synaptopathy (CS) in humans. Furthermore, we suggested this approach as a general framework for investigating behavioural tests before conducting expensive and time-consuming human experiments. We trained the DNN model on natural speech data and simulated a broadband-noise gap detection task. The trained model was sensitive to CS and hearing threshold shifts induced by inner hair cell (IHC) dysfunction. In contrast, the DNN model achieved lower gap detection thresholds with induced outer hair cell (OHC) dysfunction. We concluded that a gap detection task may be a behavioural test sensitive to CS and potentially also to IHC dysfunction.
All documentation are written in epytext markdown and can be generated using generateDocs.
A precompiled version is available here, and hosted here.
The folder contains all notebooks for using 'GapNet'.
The folders are setup as packages, i.e. they have a __init__.py for creating docs for each folder to include in the
autogenerated documentation
Everything is tested and run with python 3.10.7.