A hybrid approach to structural modeling of individualized HRTFs

Conference Paper (2021)
Authors

Riccardo Miccini (Aalborg University)

S. Spagnol (TU Delft - Form and Experience)

Research Group
Form and Experience
Copyright
© 2021 Riccardo Miccini, S. Spagnol
To reference this document use:
https://doi.org/10.1109/VRW52623.2021.00022
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Riccardo Miccini, S. Spagnol
Research Group
Form and Experience
Pages (from-to)
80-85
ISBN (electronic)
978-0-7381-1367-8
DOI:
https://doi.org/10.1109/VRW52623.2021.00022
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Abstract

We present a hybrid approach to individualized head-related transfer function (HRTF) modeling which requires only 3 anthropometric measurements and an image of the pinna. A prediction algorithm based on variational autoencoders synthesizes a pinna-related response from the image, which is used to filter a measured head-andtorso response. The interaural time difference is then manipulated to match that of the HUTUBS dataset subject minimizing the predicted localization error. The results are evaluated using spectral distortion and an auditory localization model. While the latter is inconclusive regarding the efficacy of the structural model, the former metric shows promising results with encoding HRTFs. Index Terms: Hardware - Digital signal processing; Computing methodologies - Neural networks; Applied computing - Sound and music computing

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