Remote audiogram and notched-noise tests using Gaussian processes.

The study aimed to develop and validate remote hearing detection (audiogram) and discrimination (notched-noise) tests, which used Bayesian active-learning based Gaussian processes, as a binary discriminator between audible and inaudible sounds, and to choose the stimulus parameters. Forty-two participants (aged 48-75 years) performed the two tests remotely using their own equipment. The participants were recruited with pure-tone audiometry known a priori. The agreements between the true and estimated hearing thresholds were assessed using Bland-Altman plots and concordance correlation coefficients (CCC). The notched-noise test was used to derive auditory filter shape parameters, from which equivalent rectangular bandwidths (ERBs) were estimated. The ERBs were correlated with participants' hearing thresholds and compared to published data. The agreement between the true and estimated hearing thresholds ranged between poor and fair (CCC = 0.19 and 0.34), with an average bias (±limit of agreements) of 27.3 (±17.8) dB. This was attributed to the lack of calibration of the participants' equipment. The estimated ERBs followed the trend of published data and were significantly correlated with pure-tone audiometry at 1000 Hz (rs = 0.36). Our findings indicate that the remote detection and discrimination tests can collectively track the breakpoints, slopes of thresholds, and the width of auditory filters.