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Standard audiogram classification from loudness scaling data using unsupervised, supervised, and explainable machine

Chen Xu^1,2, Lena Schell-Majoor¹, Birger Kollmeier¹

¹Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany.

International Journal of Audiology

|April 1, 2026

Summary

This summary is machine-generated.

Machine learning models can predict standard audiogram types using calibration-independent loudness data. This approach may aid remote hearing aid fitting without traditional audiograms.

Keywords:

Bisgaard profiles prediction Explainable machine learning big data loudness scaling test

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Area of Science:

Audiology
Machine Learning
Rehabilitative Audiology

Background:

Remote audiogram assessment faces calibration and procedural challenges.
Traditional audiograms are crucial for hearing aid fitting but can be difficult to obtain remotely.

Purpose of the Study:

To investigate if calibration-independent adaptive categorical loudness scaling (ACALOS) data can approximate individual audiograms.
To classify listeners into standard Bisgaard audiogram types using machine learning (ML).
To assess the feasibility of this approach for remote hearing aid fitting.

Main Methods:

Evaluated unsupervised, supervised, and explainable ML approaches.
Utilized a large auditory reference database (n=847 ears) with ACALOS data.
Employed Principal Component Analysis (PCA) and seven supervised ML classifiers, including logistic regression.

Main Results:

ML models demonstrated reasonable classification performance for Bisgaard audiogram types.
Logistic regression achieved the highest accuracy among supervised classifiers.
Minimum loudness levels at 1.5 and 4 kHz showed the highest predictive power.

Conclusions:

ML models can predict standard Bisgaard audiogram types from loudness perception data.
This method shows potential for hearing aid fitting in remote or resource-limited settings.
Calibration-independent loudness data offers a promising alternative to traditional audiograms.