Article

Towards automatic Prediction of personalized Head-related Transfer Functions: Application and Evaluation of a Parametric Pinna Model (en)

Authors

* Presenting author
Day / Time: 08.03.2023, 15:40-16:20
Room: Foyer Z, Posterinsel Q
Typ: Poster
Session: Virtuelle Akustik (Late Poster, Posterinsel Q)
Information: Die Poster werden durchgängig von Dienstag bis Donnerstag im Foyer auf Ebene Z ausgestellt (3.OG vor dem Plenarsaal Z), sortiert nach thematischen Zusammenhängen in der hier angegebenen Posterinsel (siehe Sitzungstitel). Das Posterforum zur angegebenen Zeit bietet die Möglichkeit, mit den Autor*innen in Diskussion zu kommen.
Abstract: The plausibility of virtual-reality and augmented-reality systems benefits from personalized head-related transfer functions (HRTFs). Personalized HRTFs are traditionally obtained by means of elaborate acoustic measurements in laboratory settings using special equipment, making them less accessible for the general public. As an alternative, a parametric pinna model (PPM) matching a generic mesh to an individual target pinna can be used to calculate personalized HRTFs numerically. In this contribution, we will present a framework facilitating a comprehensive application-driven evaluation of such a PPM. An important first step in the evaluation will be the estimation of PPM-parameter ranges required to cover the variety of human ears. These parameter ranges will be then used to create datasets for training deep neural networks estimating personalized parameter sets from the listener’s visual information. Our evaluations will be done in the geometric, acoustic, and perceptual domains. Further, we will outline challenges towards the PPM-based personalization of HRTFs.
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