Auditory Model Based Subsetting of Head-Related Transfer Function Datasets

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 cand...

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Bibliographic Details
Published inProceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 391 - 395
Main Author Spagnol, Simone
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2020
Subjects
Auditory model
binaural
HRTF selection
Location awareness
Prediction algorithms
Predictive models
Signal processing
sound localization
Speech processing
Transfer functions
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP40776.2020.9053360