Calibration offset estimation in mobile hearing tests via categorical loudness scaling

Abstract

To enable reliable smartphone-based hearing assessments by developing methods to estimate device calibration offsets using categorical loudness scaling (CLS). Calibration offsets were simulated from a Gaussian distribution. Two prediction models—a Bayesian regression model and a nearest neighbour model—were trained on CLS-derived parameters and data from the Oldenburg Hearing Health Record (OHHR). CLS was chosen because it provides level-independent measures (e.g., dynamic range) that remain robust despite calibration errors. 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. The Bayesian regression model achieved median absolute errors (MAEs) of about 5 dB between the estimated and “true” calibration offsets. Calibration uncertainty was reduced by factors between 0.41 and 0.79, demonstrating greater robustness in uncontrolled environments. CLS-based models show potential to compensate for missing calibration in our simulation study, but validation using uncalibrated mobile-device listening tests with real listeners is still needed. 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.

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ORCID for Chen Xu: orcid.org/0000-0003-3233-3179

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