Personalizing head related transfer functions for earables

Abstract

Head related transfer functions (HRTF) describe how sound signals bounce, scatter, and diffract when they arrive at the head, and travel towards the ears. HRTFs produce distinct sound patterns that ultimately help the brain infer the spatial properties of the sound, such as its direction of arrival, 𝜃. If an earphone can learn the HRTF, it could apply the HRTF to any sound and make that sound appear directional to the user. For instance, a directional voice guide could help a tourist navigate a new city. While past works have estimated human HRTFs, an important gap lies in personalization. Today's HRTFs are global templates that are used in all products; since human HRTFs are unique, a global HRTF only offers a coarse-grained experience. This paper shows that by moving a smartphone around the head, combined with mobile acoustic communications between the phone and the earbuds, it is possible to estimate a user's personal HRTF. Our personalization system, UNIQ, combines techniques from channel estimation, motion tracking, and signal processing, with a focus on modeling signal diffraction on the curvature of the face. The results are promising and could open new doors into the rapidly growing space of immersive AR/VR, earables, smart hearing aids, etc.