Speakers as sensors: Machine learning-based earphone impedance analysis for ear canal insertion depth prediction

This study investigates the use of speakers (earphones) as sensors for detecting acoustic loads through variations in electrical impedance. Leveraging electroacoustic coupling and machine learning-based analysis, impedance features were used to predict insertion depth with high accuracy. The dense neural network-based classification achieved 87% overall accuracy, improving to 91% with speaker-specific data and up to 99% for individual units. By identifying optimal frequency-domain regions in which impedance responses were most sensitive to changes in acoustic loading, classification performance further improved. The results were also compared with a regression model, which yielded higher error rates but indicated a promising direction for future work. This work integrated electroacoustic principles with machine learning to establish a foundation for smart, low-cost, and portable diagnostic systems for ear health and related biomedical applications.