Spatial up-sampling of HRTF sets using generative adversarial networks: a pilot study
File(s)
Published version
Author(s)
Siripornpitak, Pongsakorn
Engel, Isaac
Cooper, Sam
Squires, Isaac
Picinali, Lorenzo
Type
Journal Article
Abstract
Headphone-based spatial audio simulations rely on Head Related Transfer Functions (HRTFs) in order to reconstruct the sound field at the entrance of the listener’s ears. A HRTF is strongly dependent on the listener’s specific anatomical structures, and it has been shown that virtual sounds recreated with someone else’s HRTF result in worse localisation accuracy, as well as altering other subjective measures such as externalisation and realism. Acoustic measurements of the filtering effects generated by ears, head and torso has proven to be one of the most reliable ways to obtain a personalised HRTF. However this requires a dedicated and expensive setup, and is time-intensive. In order to simplify the measurement setup, thereby improving the scalability of the process, we are exploring strategies to reduce the number of acoustic measurements without degrading the spatial resolution of the HRTF. Traditionally, spatial up sampling of HRTF sets is achieved through barycentric interpolation or by employing the spherical harmonics framework. However, such methods often perform poorly when the provided HRTF data is spatially very sparse. This work investigates the use of generative adversarial networks (GANs) to tackle the up-sampling problem, offering an initial insight about the suitability of this technique. Numerical evaluations based on spectral magnitude error and perceptual model outputs are presented on single spatial dimensions, therefore considering sources positioned only in one of the three main planes: horizontal, median, and frontal. Results suggest that traditional HRTF interpolation methods perform better than the proposed GAN-based one when the distance between measurements is smaller than 90°, but for the sparsest conditions (i.e. one measurement every 120° to 180°), the proposed approach outperforms the others.
Date Issued
2022-08-23
Date Acceptance
2022-07-14
Citation
Frontiers in Signal Processing, 2022, 2, pp.1-10
ISSN
2673-8198
Publisher
Frontiers Media
Start Page
1
End Page
10
Journal / Book Title
Frontiers in Signal Processing
Volume
2
Copyright Statement
© 2022 Siripornpitak, Engel, Squires, Cooper and Picinali. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
License URL
Sponsor
Commission of the European Communities
Commission of the European Communities
Identifier
https://www.frontiersin.org/articles/10.3389/frsip.2022.904398/full
Grant Number
101017743
101017743
Publication Status
Published
Article Number
904398
Date Publish Online
2022-08-23