Initial evaluation of an auditory-model-aided selection procedure for non-individual HRTFs
File(s)
Published version
Author(s)
Daugintis, Rapolas
Barumerli, Roberto
Geronazzo, Michele
Picinali, Lorenzo
Type
Conference Paper
Abstract
Binaural spatial audio reproduction systems use measured
or simulated head-related transfer functions (HRTFs),
which encode the effects of the outer ear and body on
the incoming sound to recreate a realistic spatial auditory
field around the listener. The sound localisation cues embedded in the HRTF are highly personal. Establishing
perceptual similarity between different HRTFs in a reliable manner is challenging due to a combination of acoustic and non-acoustic aspects affecting our spatial auditory
perception. To account for these factors, we propose an
automated procedure to select the ‘best’ non-individual
HRTF dataset from a pool of measured ones. For a group
of human participants with their own acoustically measured HRTFs, a multi-feature Bayesian auditory sound localisation model is used to predict individual localisation
performance with the other HRTFs from within the group.
Then, the model selection of the ‘best’ and the ‘worst’
non-individual HRTFs is evaluated via an actual localisation test and a subjective audio quality assessment in comparison with individual HRTFs. A successful model-aided
objective selection of the ‘best’ non-individual HRTF may
provide relevant insights for effective and handy binaural spatial audio solutions in virtual/augmented reality
(VR/AR) applications.
or simulated head-related transfer functions (HRTFs),
which encode the effects of the outer ear and body on
the incoming sound to recreate a realistic spatial auditory
field around the listener. The sound localisation cues embedded in the HRTF are highly personal. Establishing
perceptual similarity between different HRTFs in a reliable manner is challenging due to a combination of acoustic and non-acoustic aspects affecting our spatial auditory
perception. To account for these factors, we propose an
automated procedure to select the ‘best’ non-individual
HRTF dataset from a pool of measured ones. For a group
of human participants with their own acoustically measured HRTFs, a multi-feature Bayesian auditory sound localisation model is used to predict individual localisation
performance with the other HRTFs from within the group.
Then, the model selection of the ‘best’ and the ‘worst’
non-individual HRTFs is evaluated via an actual localisation test and a subjective audio quality assessment in comparison with individual HRTFs. A successful model-aided
objective selection of the ‘best’ non-individual HRTF may
provide relevant insights for effective and handy binaural spatial audio solutions in virtual/augmented reality
(VR/AR) applications.
Date Acceptance
2023-07-19
Citation
Proceedings of Forum Acusticum, pp.1-8
ISSN
2221-3767
Start Page
1
End Page
8
Journal / Book Title
Proceedings of Forum Acusticum
Copyright Statement
©2023 Rapolas Daugintis et al. This is an openaccess article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Source
Forum Acusticum
Start Date
2023-09-10
Finish Date
2023-09-15
Coverage Spatial
Turin, Italy