This paper makes use of a new dataset of Head-Related Transfer Functions (HRTFs) containing high resolution median-plane acoustical measurements of a KEMAR mannequin with 20 different left pinna models as well as 3D scans of the same pinna models. This allows for an investigation of the relationship between 3D ear features and the first pinna notch present in the HRTFs, with the final aim of developing an accurate and handy procedure for predicting the individual HRTF from non-acoustical measurements. We propose a method that takes the 3D pinna mesh and generates a dataset of depth maps of the pinna viewed from various median-plane elevation angles, each having an associated pinna notch frequency value as identified in the HRTF measurements. A multiple linear regression model is then fit to the depth maps, aiming to predict the corresponding first pinna notch. The results of the regression model show moderate improvement to similar previous work built on global and elevation-dependent anthropometric pinna features extracted from 2D images.
|Title of host publication||SMC 2020 - Proceedings of the 17th Sound and Music Computing Conference|
|Editors||Simone Spagnol, Andrea Valle|
|Number of pages||7|
|Publication status||Published - 2020|
|Event||17th Sound and Music Computing Conference, SMC 2020 - Virtual, Torino, Italy|
Duration: 24 Jun 2020 → 26 Jun 2020
|Name||Proceedings of the Sound and Music Computing Conferences|
|Conference||17th Sound and Music Computing Conference, SMC 2020|
|Period||24/06/20 → 26/06/20|
Bibliographical notePublisher Copyright:
Copyright © 2020 Marius George Onofrei, Riccardo Miccini et al.