Calibration offset estimation in mobile hearing tests via categorical loudness scaling

TL;DR

This study develops and evaluates models using categorical loudness scaling to estimate and correct calibration offsets in smartphone hearing tests, enabling more reliable assessments outside labs.

Contribution

It introduces CLS-based prediction models that accurately estimate calibration errors, improving mobile hearing test reliability in uncontrolled environments.

Findings

Bayesian regression achieved up to 0.81 correlation with true offsets.

Calibration uncertainty reduced by factors of 0.41 to 0.79.

CLS-based models outperform threshold-based approaches.

Abstract

Objective: To enable reliable smartphone-based hearing assessments by developing methods to estimate device calibration offsets using categorical loudness scaling (CLS). Design: Calibration offsets were simulated from a Gaussian distribution. Two prediction models - a Bayesian regression model and a nearest neighbor model - were trained on CLS-derived parameters and data from the Oldenburg Hearing Health Repository (OHHR). CLS was chosen because it provides level-independent measures (e.g., dynamic range) that remain robust despite calibration errors. Study Sample: The dataset comprised CLS results from N = 847 participants with a mean age of 70.0 years (SD = 8.7), including 556 male and 291 female listeners with diverse hearing profiles. Results: The Bayesian regression model achieved correlations of up to 0.81 between estimated and true calibration offsets, enabling accurate individual-level correction. Compared to threshold-based approaches, calibration uncertainty was reduced by factors between 0.41 and 0.79, demonstrating greater robustness in uncontrolled environments. Conclusions: CLS-based models can effectively compensate for missing calibration in mobile hearing assessments. This approach provides a practical alternative to threshold-based methods, supporting the use of smartphone-based tests outside laboratory settings and expanding access to reliable hearing healthcare in everyday and resource-limited contexts.