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The ability to generate appropriate auditory localization cues is an important requisite of spatial audio rendering technology that contributes to the plausibility of virtual sounds presented to a user, especially in XR applications (VR/AR/MR). Algorithmic approaches have been proposed to quantify such technologies’ ability to reproduce interaural level difference (ILD) cues through regression and statistical methods, providing a useful standardization and automation method to estimate the localization accuracy potential of a given spatial audio rendering engine. Previous approaches are extended to include interaural time difference (ITD) cues as part the perceptual transform through the use of the interaural transfer function (ITF). The extended algorithmic approach of quantifying localization accuracy may provide an adequate substitute for critical listening studies as an evaluation method. However, this approach has not yet been validated through comparison with localization listening studies. A review of listening tests are reviewed in conclusion to increase confidence in presented methods of algorithmically quantifying localization accuracy potential of a spatial audio rendering engine.
Author (s): Mathew, Justin D; Januszkiewicz, Lukasz; Pensko, Maria; Hertensteiner, Mark; Audfray, Rémi
Affiliation:
Magic Leap; SoftServe Inc.; Facebook Reality Labs
(See document for exact affiliation information.)
AES Convention: 151
Paper Number:648
Publication Date:
2021-10-06
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Session subject:
Multichannel and spatial audio processing and applications
Permalink: https://aes2.org/publications/elibrary-page/?id=21511
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Mathew, Justin D; Januszkiewicz, Lukasz; Pensko, Maria; Hertensteiner, Mark; Audfray, Rémi; 2021; Quantifying Localization Potential using Interaural Transfer Function [PDF]; Magic Leap; SoftServe Inc.; Facebook Reality Labs; Paper 648; Available from: https://aes2.org/publications/elibrary-page/?id=21511
Mathew, Justin D; Januszkiewicz, Lukasz; Pensko, Maria; Hertensteiner, Mark; Audfray, Rémi; Quantifying Localization Potential using Interaural Transfer Function [PDF]; Magic Leap; SoftServe Inc.; Facebook Reality Labs; Paper 648; 2021 Available: https://aes2.org/publications/elibrary-page/?id=21511
@article{mathew2021quantifying,
author={mathew justin d and januszkiewicz lukasz and pensko maria and hertensteiner mark and audfray rémi},
journal={journal of the audio engineering society},
title={quantifying localization potential using interaural transfer function},
year={2021},
number={648},
month={october},}