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 whi