Technology Category
Machine Learning: Calibration & Uncertainty QuantificationHumans and AI: Human-in-the-loop Machine LearningKnowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
Standard audiogram classification from loudness scaling data using unsupervised, supervised, and explainable machine learning techniques
🤖 AI Summary
Traditional audiogram testing in remote hearing rehabilitation relies on calibrated equipment and complex procedures, limiting accessibility. Method: This study proposes a machine learning framework to automatically classify hearing-impaired individuals into standard Bisgaard audiogram types using calibration-free Adaptive Categorical Loudness Scaling (ACALOS) data. It integrates unsupervised clustering, seven multiclass classifiers—including logistic regression—and explainable AI techniques, with PCA dimensionality reduction (first two components explaining >50% variance) applied to a large-scale ACALOS dataset (N=847). Contribution/Results: Logistic regression achieved the highest classification accuracy, demonstrating that clinical audiogram typology can be reliably predicted solely from loudness perception data. This approach eliminates hardware calibration requirements and establishes a novel, scalable paradigm for hearing assessment in resource-constrained or remote settings.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Classify audiogram types from loudness scaling data using machine learning
Evaluate unsupervised, supervised, and explainable machine learning techniques
Predict standard Bisgaard audiogram types without calibration for remote settings
Innovation
Methods, ideas, or system contributions that make the work stand out.
Using unsupervised, supervised, and explainable machine learning techniques
Classifying audiogram types from calibration-independent loudness scaling data
Applying PCA and logistic regression for remote audiogram assessment
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