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

Key Points

• This research aims to explore how machine learning models can predict audiogram types from loudness scaling data.
• Utilized unsupervised and supervised machine learning techniques
• Analyzed calibration-independent loudness perception data
• Developed models to classify audiogram types
• Machine learning models successfully predicted audiogram types with certain limitations
• Potential to support hearing aid fittings in remote settings without traditional audiograms