3D ear shape as an estimator of HRTF notch frequency

Marius George Onofrei, Riccardo Miccini, Rúnar Unnthórsson, Stefania Serafin, Simone Spagnol

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)
82 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of the 17th Sound & Music Computing Conference (SMC 2020)
EditorsSimone Spagnol, Andrea Valle
Number of pages7
Place of PublicationTorino, Italy
PublisherAxea sas/SMC Network
Publication date24 Jun 2020
ISBN (Electronic)978-88-945415-0-2
Publication statusPublished - 24 Jun 2020
Event17th Sound and Music Computing Conference - Torino, Italy
Duration: 24 Jun 202026 Jun 2020
Conference number: 17


Conference17th Sound and Music Computing Conference
Internet address
SeriesProceedings of the Sound and Music Computing Conference


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