Parametric pinna model for a realistic representation of listener-specific pinna geometry

Personalised binaural audio requires individual head-related transfer functions (HRTFs). Nowadays, it is feasible to compute HRTFs numerically from meshes of a listener’s anatomy. These meshes require geometric details of the pinna which are difficult to capture and often corrupted by noise and outliers. The alignment of a highresolution template geometry to the captured listener geometry seems to be a promising approach to deal with these corruptions. However, such an alignment represents a multi-dimensional optimisation problem. To tackle this, we describe a parametric pinna model which reduces the representation of a non-pathological human pinna geometry from a few thousand points in space to 144 parameter dimensions. The model was evaluated in the geometric and psychoacoustic domains in terms of the Hausdorff distance distribution and simulated localisation errors, respectively. When aligned with individual ground-truth meshes, the parametric pinna model was able to represent these meshes with a small geometric error. The predicted sound-localisation performance, using the aligned meshes as a basis for HRTF calculations, was similar to that of the corresponding listener. These results indicate that the proposed parametric pinna model represents a versatile tool to create human pinna meshes with a detailed geometry for the calculation of individual HRTFs.