Anthropometric Features Estimation Using Integrated Sensors on a Headphone for HRTF Personalization

Personalization of HRTF is essential for spatial sound rendering, for which a possible solution is based on one or more anthropological measures of the subject. Measuring these anthropometrics seamlessly, accurately and reliably is still a challenge. In this paper, we propose a system for obtaining anthropometric measurements, suitable for HRTF personalization, directly from a high-end headphone. The proposed system is multi-modal and leverages existing sensors to extract features related to listener’s head dimensions. We propose three signal processing methodologies for three modalities of sensors and a fusion algorithm to aggregate these extracted features for a robust anthropometry estimation. To verify the design we use a data set, collected from 35 subjects. The proposed algorithm achieves a low error (RMSE) of 0.58−1.21 cm for human anthropometry estimation.