• Riccardo Miccini
4. Term, Sound and Music Computing (Master Programme)
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.
Publication date2020
Number of pages72
ID: 403256658