Audiogram estimation using Bayesian active learning

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.