A hybrid approach to structural modeling of individualized HRTFs: Generating and combining pinna responses, head-and-torso filtering, and interaural time difference data
Translated title
A hybrid approach to structural modeling of individualized HRTFs
Author
Term
4. Term
Education
Publication year
2020
Submitted on
2021-01-31
Pages
72
Abstract
Providing users with a personalized HRTF set is paramount for an immersive VR experience, free from localization errors and inside-the-head sound perception. However, direct acoustic measurement of the user's HRTF requires specialized apparatuses and is often strenuous and expensive. We present a hybrid approach to HRTF modeling which requires only 3 anthropometric measurements and an image on the pinna contours. A prediction algorithm based on variational autoencoders synthesizes a pinna response from its contours, which is used to filter a measured head-and-torso response. The ITD is then manipulated to match that of a HUTUBS dataset subject minimizing the predicted localization error. The performances are evaluated using spectral distortion and a perceptual localization model. While the latter is inconclusive regarding the efficacy of the structural model, the former metric shows promising results with encoding HRTF datasets.
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