The acquisition of the human pinna geometry requires elaborate equipment for accurate results. Even then, the results are often corrupted by measurement artifacts. We introduce Mesh2PPM, a framework facilitating the generation of a personalized and artifact-free pinna mesh. Mesh2PPM predicts the parameters of a parametric pinna model based on cubic Bézier curves (BezierPPM) from a pinna mesh via a vision transformer. We evaluated Mesh2PPM with multi-view renderings of synthetic pinna geometries, providing additional depth information, varying the grids of camera views, and jittering the camera views. While added depth information had no practically relevant effect, a grid with 3 ×3 camera views facilitated the lowest overall prediction errors and best counteracted the detrimental effects of jitter. For this grid, with and without jitter, the median Pompeiu-Hausdorff distances were 1.98 mm and 1.34 mm, respectively, and the root mean square distances were 0.92 mm and 0.52 mm. A refined analysis targeting the perceptually most important pinna regions for sound localization showed that multi-view information particularly improved the prediction of BezierPPM parameters describing the cavum-conchae region. The accuracy achieved indicates the suitability of Mesh2PPM to retrieve BezierPPM parameters from pinna meshes.

Since the creation of the spatially oriented format for acoustics (SOFA, Audio Engineering Society standard AES69), numerous databases of head-related transfer functions (HRTFs) are now available as standardized SOFA files. However, the methodologies for measuring and postprocessing HRTFs vary significantly across laboratories. This leads to objective and perceptual inconsistencies between HRTF databases and makes it challenging to integrate multiple databases into a single repository to facilitate wide-scale research and application. This paper introduces a normalization procedure, applicable to any HRTF data set, aimed at enhancing the consistency across HRTF data sets obtained from different laboratories while preserving the spatial information essential to HRTFs. The proposed approach consists of six processing steps: low-pass filtering, temporal alignment, temporal windowing, diffuse-field equalization, low-frequency extrapolation, and far-field correction. The normalization was evaluated on 17 HRTF data sets of the same dummy head by means of acoustic analyses and auditory simulations and further validated with respect to a database of 54 human subjects. Results show that the proposed normalization improves data set applicability and consistency while maintaining the directional cues within each data set.