@inproceedings{c174614d4c0f4dae845bcceea7569cf9,
title = "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.",
keywords = "Auditory model, HRTF selection, binaural, sound localization",
author = "Simone Spagnol",
year = "2020",
month = may,
doi = "10.1109/icassp40776.2020.9053360",
language = "English",
series = "I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings",
publisher = "IEEE",
pages = "391--395",
booktitle = "Proceedings of the 45th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020)",
address = "United States",
note = "ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; Conference date: 04-05-2020 Through 08-05-2020",
}