Audiogram estimation using Bayesian active learning

Abstract

Two methods for estimating audiograms quickly and accurately using Bayesian active learning were developed and evaluated. Both methods provided an estimate of threshold as a continuous function of frequency. For one method, six successive tones with decreasing levels were presented on each trial and the task was to count the number of tones heard. A Gaussian Process was used for classification and maximum-information sampling to determine the frequency and levels of the stimuli for the next trial. The other method was based on a published method using a Yes/No task but extended to account for lapses. The obtained audiograms were compared to conventional audiograms for 40 ears, 19 of which were hearing impaired. The threshold estimates for the active-learning methods were systematically from 2 to 4 dB below (better than) those for the conventional audiograms, which may indicate a less conservative response criterion (a greater willingness to respond for a given amount of sensory information). Both active-learning methods were able to allow for wrong button presses (due to lapses of attention) and provided accurate audiogram estimates in less than 50 trials or 4 min. For a given level of accuracy, the counting task was slightly quicker than the Yes/No task.