A hybrid approach to structural modeling of individualized HRTFs

Conference paper (2021)

Authors

Riccardo Miccini Aalborg University
S. Spagnol Design Aesthetics - IDE
Research Group
Design Aesthetics (IDE) (TU Delft)
Copyright: © 2021 Riccardo Miccini, S. Spagnol
DOI
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https://doi.org/10.1109/VRW52623.2021.00022
Digital signal processing Neural networks Sound and music computing
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Industrial Design Engineering

Department

Human-Centered Design

Research Group

Design Aesthetics

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

Files

VRW_2021.pdf
(.pdf | 1.39 Mb)