A hybrid approach to structural modeling of individualized HRTFs

Riccardo Miccini, Simone Spagnol

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
8 Downloads (Pure)

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

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages80-85
Number of pages6
ISBN (Electronic)978-0-7381-1367-8
DOIs
Publication statusPublished - 2021
Event2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021 - Virtual, Lisbon, Portugal
Duration: 27 Mar 20213 Apr 2021

Publication series

NameProceedings - 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021

Conference

Conference2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021
CountryPortugal
CityVirtual, Lisbon
Period27/03/213/04/21

Keywords

  • Digital signal processing
  • Neural networks
  • Sound and music computing

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