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

Abstract

To address the calibration and procedural challenges inherent in remote audiogram assessment for rehabilitative audiology, this study investigated whether calibration-independent adaptive categorical loudness scaling (ACALOS) data can be used to approximate individual audiograms by classifying listeners into standard Bisgaard audiogram types using machine learning. Three classes of machine learning approaches - unsupervised, supervised, and explainable - were evaluated. Principal component analysis (PCA) was performed to extract the first two principal components, which together explained more than 50 percent of the variance. Seven supervised multi-class classifiers were trained and compared, alongside unsupervised and explainable methods. Model development and evaluation used a large auditory reference database containing ACALOS data (N = 847). The PCA factor map showed substantial overlap between listeners, indicating that cleanly separating participants into six Bisgaard classes based solely on their loudness patterns is challenging. Nevertheless, the models demonstrated reasonable classification performance, with logistic regression achieving the highest accuracy among supervised approaches. These findings demonstrate that machine learning models can predict standard Bisgaard audiogram types, within certain limits, from calibration-independent loudness perception data, supporting potential applications in remote or resource-limited settings without requiring a traditional audiogram.