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Classifying Non-Individual Head-Related Transfer Functions with A Computational Auditory Model: Calibration And Metrics

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DOI:
10.60864/fjez-9845
Citation Author(s):
Rapolas Daugintis
Rapolas Daugintis, Roberto Barumerli, Lorenzo Picinali, Michele Geronazzo
Submitted by:
Rapolas Daugintis
Last updated:
17 November 2023 - 12:07pm
Document Type:
Research Manuscript
Document Year:
2023
Event:
ICASSP 2023
Presenters:
Rapolas Daugintis
Paper Code:
4813
Categories:
Spatial and Multichannel Audio
Auditory Modeling and Hearing Aids
 

This study explores the use of a multi-feature Bayesian auditory sound localisation model to classify non-individual head-related transfer functions (HRTFs). Based on predicted sound localisation performance, these are grouped into ‘good’ and ‘bad’, and the ‘best’/‘worst’ is selected from each category. Firstly, we present a greedy algorithm for automated individual calibration of the model based on the individual sound localisation data. We then discuss data analysis of predicted directional localisation errors and present an algorithm for categorising the HRTFs based on the localisation error distributions within a limited range of directions in front of the listener. Finally, we discuss the validity of the classification algorithm when using averaged instead of individual model parameters. This analysis of auditory modelling results aims to provide a perceptual foundation for automated HRTF personalisation techniques for an improved experience of binaural spatial audio technologies.

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