Acoustic selfies for extraction of external ear features in mobile audio augmented reality

Abstract

Virtual and augmented realities are expected to become more and more important in everyday life in the next future; the role of spatial audio technologies over headphones will be pivotal for application scenarios which involve mobility. This paper introduces the SelfEar project, aimed at low-cost acquisition and personalization of Head-Related Transfer Functions (HRTFs) on mobile devices. This first version focuses on capturing individual spectral features which characterize external ear acoustics, through a self-adjustable procedure which guides users in collecting such information: their mobile device must be held with the stretched arm and positioned at several specific elevation points; acoustic data are acquired by an audio augmented reality headset which embeds a pair of microphones at listener ear-canals. A preliminary measurement session assesses the ability of the system to capture spectral features which are crucial for elevation perception. Moreover, a virtual experiment using a computational auditory model predicts clear vertical localization cues in the measured features.