Model-based hearing aid gain prescription rule
Abstract
In listeners with sensorineural hearing loss, loudness recruitment is typically observed and can be attributed to a loss or dysfunction of the outer hair cells (OHC). On the level of the basilar membrane (BM), OHC loss results in a reduced gain for low-level signals, changing the BM input-output function. This amount of low-level gain loss cannot be directly estimated from the overall hearing loss as characterized by pure-tone audiometric thresholds. However, from a modelling perspective, a hearing aid might be successful if it is able to compensate the gain loss and thus restore the compressive BM input-output function of a normal-hearing listener. Here, psychoacoustic temporal masking curves (TMC) and adaptive categorical loudness scaling data (ACALOS) of the same normal-hearing (NH) and hearing-impaired (HI) subjects were used to estimate gain loss. A linear model was fitted to predict gain loss from audiometric thresholds and from the steepness of the loudness function. Comparison of the predicted gain loss in HI and the gain in NH lead to a gain prescription rule. This prescription was tested with a conventional hearing-aid compressor and a model-based version, which compares simple NH and HI auditory models in real time.
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