On the Improvement of Auditory Accuracy with Non-Indivisualized HRTF-Based Sounds
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.
Click to purchase paper or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members, $5 for AES members and is free for E-Library subscribers.