Perceptually based head-related transfer function database optimization

Abstract

In the context of binaural audio rendering, choosing the best head-related transfer function (HRTF) for an individual from large databases poses several problems. This study proposes a method to reduce the size of a given HRTF database. Participants, 45 in total, were asked to rate the quality of binaural synthesis for 46 HRTFs. The lack of reciprocity in the ratings was noted. Results were used to create a perceptually optimized HRTF subset which satisfied all participants’ judgments. The subset was validated using localization tests on a separate group of subjects with results showing reduced errors when subjects were given their best choice, rather than their worst choice HRTF.