Estimation of parameters of a Head-Related Transfer Function (HRTF) customization model

Abstract

Head-Related Transfer Functions (HRTFs) are special digital filters used to create the effect of three-dimensional (3D) virtual sound source placement over headphones. The two most common methods of obtaining HRTFs are to either individually measure the HRTFs on specialized equipment (individualized HRTFs) or to create a set of generic HRTFs by measuring them on a mannequin with average anatomical features (generic HRTFs). Individualized HRTFs required specialized equipment that is not readily available to the general public. Additionally, it is known that HRTFs are heavily dependent on our anatomical features. As a result, generic HRTFs produce significant localization errors. A multi-linear model is now available which uses simple anthropometric measurements of the intended user's anatomy to generate customized HRTFs. These customized HRTFs can be generated without specialized equipment and have improved spatialization over generic HRTFs. However, the anthropometric measurements, which are used as parameters for the customization model, are currently collected manually. In the present work, computer vision techniques are used to automatically estimate a portion of the anthropometric measurements of the human pinnae. Analysis of the estimation technique's performance will also be conducted.