Remote audiogram and notched-noise tests using Gaussian processes

Emanuele Perugia; Michael Stone; Karolina Kluk-De Kort; Josef Schlittenlacher

doi:10.1121/10.0042400

Remote audiogram and notched-noise tests using Gaussian processes

Emanuele Perugia^*
, Michael Stone
, Karolina Kluk-De Kort
, Josef Schlittenlacher^*

^*Corresponding author for this work

Division of Psychology Communication and Human Neuroscience (L5)

Research output: Contribution to journal › Article › peer-review

Abstract

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 stimuli 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 analyses 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 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.

Original language	English
Journal	Acoustical Society of America. Journal
DOIs	https://doi.org/10.1121/10.0042400
Publication status	Published - 1 Feb 2026

Keywords

audiogram
notched-noise
gaussian processes
online test

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10.1121/10.0042400

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NIHR Manchester Biomedical Research Centre
Bruce, I. (PI), Lord, G. (CoI), Lennon, R. (CoI), Black, G. (CoI), Wedge, D. (CoI), Morris, A. (CoI), Hussell, T. (CoI), Sharrocks, A. (CoI), Stivaros, S. (CoI), Buch, M. (CoI), Gough, J. (CoI), Kostarelos, K. (CoI), Thistlethwaite, F. (CoI), Kadler, K. (CoI), Barton, A. (CoI), Hyrich, K. (CoI), Mcbeth, J. (CoI), O'Neill, T. (CoI), Vestbo, J. (CoI), Simpson, A. (CoI), Singh, S. (CoI), Smith, J. (CoI), Felton, T. (CoI), Murray, C. (CoI), Griffiths, C. (CoI), Cullum, N. (CoI), Rhodes, L. (CoI), Warren, R. (CoI), Paus, R. (CoI), Dumville, J. (CoI), Viros Usandizaga, A. (CoI), Keavney, B. (CoI), Tomaszewski, M. (CoI), Allan, S. (CoI), Body, R. (CoI), Cartwright, E. (CoI), Heagerty, A. (CoI), Kalra, P. (CoI), Miller, C. (CoI), Rutter, M. (CoI), Smith, C. (CoI), Trafford, A. (CoI), Evans, D. (CoI), Davidson, E. (CoI), Crosbie, P. (CoI), Harvie, M. (CoI), Howell, S. (CoI), Renehan, A. (CoI), Dive, C. (CoI), Blackhall, F. (CoI), Landers, D. (CoI), Krebs, M. (CoI), Cook, N. (CoI), Clarke, R. (CoI), Taylor, S. (CoI), Jorgensen, C. (CoI), Lorigan, P. (CoI), Jayson, G. (CoI), Valle, J. (CoI), Mccabe, M. (CoI), Armstrong, A. (CoI), Freitas, A. (CoI), Illidge, T. (CoI), Choudhury, A. (CoI), Hoskin, P. (CoI), West, C. (CoI), Van Herk, M. (CoI), Faivre-Finn, C. (CoI), Bristow, R. (CoI), Kirkby, K. (CoI), Birtle, A. (CoI), Mackay, R. (CoI), Radford, J. (CoI), Linton, K. (CoI), Higham, C. (CoI), Munro, K. (CoI), Plack, C. (CoI), Arden Armitage, C. (CoI), Bruce, I. (CoI), Moore, D. (CoI), Saunders, G. (CoI), Stone, M. (CoI), Haddock, G. (CoI), Lewis, S. (CoI), Elliott, R. (CoI), Green, J. (CoI), Lovell, K. (CoI), Morrison, A. (CoI), Shaw, J. (CoI), Bucci, S. (CoI), Ainsworth, J. (CoI), Webb, R. (CoI), Newman, W. (CoI), Banka, S. (CoI), Clayton-Smith, J. (CoI), Payne, K. (CoI), Moldovan, R. (CoI), Wynn, R. (CoI) & Jones, S. (CoI)
1/12/22 → 30/11/27
Project: Research
Manchester Centre for Audiology and Deafness (ManCAD)
Munro, K. (PI), Millman, R. (PI), Lamb, W. (Support team), Dawes, P. (PI), Plack, C. (PI), Stone, M. (PI), Kluk-De Kort, K. (PI), Moore, D. (PI), Morton, C. (PI), Prendergast, G. (PI), Couth, S. (PI), Schlittenlacher, J. (PI), Chilton, H. (PI), Visram, A. (Researcher), Dillon, H. (PI), Guest, H. (Researcher), Heinrich, A. (PI), Littlejohn, J. (Researcher), Jones, L. (PI), Lough, M. (Researcher), Morgan, R. (Researcher), Perugia, E. (Researcher), Roughley, A. (Researcher), Whiston, H. (Researcher), Wright, C. (Support team), Saunders, G. (PI), Kelly, C. (PI), Cross, H. (Researcher), Loughran, M. (Researcher), Hoseinabadi, R. (PI) & Vercammen, C. (PI)
Project: Research

Cite this

@article{14c2029db5614c9c8726d3889f521ae4,

title = "Remote audiogram and notched-noise tests using Gaussian processes",

abstract = "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 stimuli 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 analyses 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{\textquoteright} 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 participants{\textquoteright} 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. ",

keywords = "audiogram, notched-noise, gaussian processes, online test",

author = "Emanuele Perugia and Michael Stone and \{Kluk-De Kort\}, Karolina and Josef Schlittenlacher",

year = "2026",

month = feb,

day = "1",

doi = "10.1121/10.0042400",

language = "English",

journal = "Acoustical Society of America. Journal",

issn = "0001-4966",

publisher = "Acoustical Society of America",

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TY - JOUR

T1 - Remote audiogram and notched-noise tests using Gaussian processes

AU - Perugia, Emanuele

AU - Stone, Michael

AU - Kluk-De Kort, Karolina

AU - Schlittenlacher, Josef

PY - 2026/2/1

Y1 - 2026/2/1

N2 - 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 stimuli 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 analyses 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 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.

AB - 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 stimuli 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 analyses 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 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.

KW - audiogram

KW - notched-noise

KW - gaussian processes

KW - online test

U2 - 10.1121/10.0042400

DO - 10.1121/10.0042400

M3 - Article

SN - 0001-4966

JO - Acoustical Society of America. Journal

JF - Acoustical Society of America. Journal

ER -

Remote audiogram and notched-noise tests using Gaussian processes

Abstract

Keywords

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Projects

NIHR Manchester Biomedical Research Centre

Manchester Centre for Audiology and Deafness (ManCAD)

Cite this