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Remote audiogram and notched-noise tests using Gaussian processes.

Emanuele Perugia1, Michael A Stone1,2, Karolina Kluk1

  • 1Manchester Centre for Audiology and Deafness, Division of Psychology, Communication and Human Neuroscience, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.

The Journal of the Acoustical Society of America
|February 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Remote hearing tests using Bayesian methods show potential for tracking hearing loss and auditory filter width. However, equipment calibration issues limited accuracy in this study.

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Area of Science:

  • Audiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Accurate hearing assessment is crucial for managing age-related hearing loss.
  • Remote testing offers convenience but faces challenges in reliability and calibration.
  • Bayesian active-learning methods show promise for optimizing audiological tests.

Purpose of the Study:

  • To develop and validate remote hearing detection (audiogram) and discrimination (notched-noise) tests.
  • To utilize Bayesian active-learning Gaussian processes for binary discrimination of sounds.
  • To select optimal stimulus parameters for remote audiological assessments.

Main Methods:

  • Developed remote audiogram and notched-noise tests employing Bayesian active-learning Gaussian processes.
  • Recruited 42 participants (48-75 years) with known pure-tone audiometry results.
  • Assessed agreement using Bland-Altman plots and concordance correlation coefficients (CCC).

    • Main Results:

    • Agreement between true and estimated hearing thresholds was poor to fair (CCC=0.19-0.34), attributed to uncalibrated participant equipment.
    • Estimated equivalent rectangular bandwidths (ERBs) correlated significantly with pure-tone audiometry at 1000 Hz (rs=0.36).
    • Remote tests collectively tracked hearing threshold breakpoints, slopes, and auditory filter width trends.

    Conclusions:

    • Remote hearing detection and discrimination tests show potential for monitoring hearing characteristics.
    • Equipment calibration is critical for improving the accuracy of remote audiological assessments.
    • The developed methods can provide insights into auditory filter shape and hearing thresholds remotely.