On the improvement of localization accuracy with nonindividualized HRTF-based sounds

Abstract

Auralization is a powerful tool to increase the realism and sense of immersion in Virtual Reality environments. The Head Related Transfer Function (HRTF) filters commonly used for auralization are non-individualized, as obtaining individualized HRTFs poses very serious practical difficulties. It is therefore extremely important to understand to what extent this hinders sound perception. In this paper, we address this issue from a learning perspective. In a set of experiments, we observed that mere exposure to virtual sounds processed with generic HRTF did not improve the subjects’ performance in sound source localization, but short training periods involving active learning and feedback led to significantly better results. We propose that using auralization with non-individualized HRTF should always be preceded by a learning period.