Volume 6, 2022
Topical Issue - Auditory models: from binaural processing to multimodal cognition
|Number of page(s)||18|
|Published online||26 January 2022|
Assessing HRTF preprocessing methods for Ambisonics rendering through perceptual models
Imperial College London, London SW7 2AZ, United Kingdom
* Corresponding author: firstname.lastname@example.org
Accepted: 9 December 2021
Binaural rendering of Ambisonics signals is a common way to reproduce spatial audio content. Processing Ambisonics signals at low spatial orders is desirable in order to reduce complexity, although it may degrade the perceived quality, in part due to the mismatch that occurs when a low-order Ambisonics signal is paired with a spatially dense head-related transfer function (HRTF). In order to alleviate this issue, the HRTF may be preprocessed so its spatial order is reduced. Several preprocessing methods have been proposed, but they have not been thoroughly compared yet. In this study, nine HRTF preprocessing methods were used to render anechoic binaural signals from Ambisonics representations of orders 1 to 44, and these were compared through perceptual hearing models in terms of localisation performance, externalisation and speech reception. This assessment was supported by numerical analyses of HRTF interpolation errors, interaural differences, perceptually-relevant spectral differences, and loudness stability. Models predicted that the binaural renderings’ accuracy increased with spatial order, as expected. A notable effect of the preprocessing method was observed: whereas all methods performed similarly at the highest spatial orders, some were considerably better at lower orders. A newly proposed method, BiMagLS, displayed the best performance overall and is recommended for the rendering of bilateral Ambisonics signals. The results, which were in line with previous literature, indirectly validate the perceptual models’ ability to predict listeners’ responses in a consistent and explicable manner.
Key words: Binaural models / Ambisonics / HRTF preprocessing / Spatial audio / Binaural rendering
© I. Engel et al., Published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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