Head-Related Transfer Functions (HRTFs) individualization is required to achieve high quality Virtual Auditory Spaces. An alternative to acoustic measurements is the customization of non-individual HRTFs. To transform HRTF data, we propose a combination of frequency scaling and rotation shift, whose parameters are predicted by a new morphological matching method. For six subjects, mesh models of head and pinnae are acquired, and differences in size and orientation of the pinnae are evaluated with a modified Iterative Closest Point (ICP) algorithm. Optimal HRTF transformations are computed in parallel. A relatively good correlation between morphological and transformation parameters is found and allows to predict the customization parameters from the registration of pinna shapes. The resulting model achieves better customization than frequency scaling only, which shows that adding the rotation degree of freedom improves HRTF individualization.
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