Enhancing vertical localization with image-guided selection of non-individual head-related transfer functions

A novel approach to the selection of generic head-related transfer functions (HRTFs) for binaural audio rendering through headphones is formalized and described in this paper. A reflection model applied to the user's ear picture facilitates extraction of the relevant anthropometric cues that are used for selecting two HRTF sets in a database fitting that user, whose localization performances are evaluated in a complete psychoacoustic experiment. The proposed selection increases the average elevation performances of 17% (with a peak of 34%) with respect to generic HRTFs from an anthropomorphic mannequin. It also significantly enhances externalization and reduces the number of up/down reversals.