Auditory Model Based Subsetting of Head-Related Transfer Function Datasets

Abstract

The rising availability of public head-related transfer function (HRTF) data, measured on hundreds of different individuals, offers a user the possibility to select the best matching non-individual HRTF from a wide catalogue. To this end, reducing the number of alternatives to a small subset of candidate HRTFs is the first step towards an efficient selection process. In this article a novel HRTF subset selection algorithm based on auditory-model vertical localization predictions and a greedy heuristic is outlined, designed to identify a representative HRTF subset from a catalogue including the three biggest public datasets currently available (373 HRTFs overall). The so-resulting subset (6 HRTFs) is then evaluated on a fourth independent dataset. Auditory model predictions show that for over 95% of the subjects of this dataset there exists at least one HRTF out of the representative subset scoring minimal vertical localization error deviations compared to the best available non-individual HRTF out of the catalogue.