RJ Hughes
Dual frequency band amplitude panning for multichannel audio systems
Hughes, RJ; Franck, A; Cox, TJ; Shirley, BG; Fazi, FM
Authors
A Franck
Prof Trevor Cox T.J.Cox@salford.ac.uk
Professor
Dr Ben Shirley B.G.Shirley@salford.ac.uk
Reader
FM Fazi
Abstract
Panning laws for multi-loudspeaker setups, for example vector base amplitude panning, are typically derived based
on either low or high frequency assumptions. It is well known, however, that auditory cues for both localization and loudness differ at these frequencies. This paper investigates the use of dual-band panning, whereby low and high frequency gains and normalization are applied separately. Single- and dual-band rendering algorithms are described, with predicted theoretical localization, spectral balance, and source extent cues given. A multiple stimulus test is described, performed for a number of trajectories rendered to a periphonic nine-loudspeaker ITU-R BS.2051-1 setup, with rated attributes based on predicted sources of error. Results are reported, detailing differences between both theoretical predictions and systems under test.
Citation
Hughes, R., Franck, A., Cox, T., Shirley, B., & Fazi, F. Dual frequency band amplitude panning for multichannel audio systems. Presented at 2018 AES International Conference on Spatial Reproduction - Aesthetics and Science, Tokyo, Japan
Presentation Conference Type | Other |
---|---|
Conference Name | 2018 AES International Conference on Spatial Reproduction - Aesthetics and Science |
Conference Location | Tokyo, Japan |
Publication Date | Jul 30, 2018 |
Deposit Date | Dec 11, 2019 |
Publisher | Audio Engineering Society |
Publisher URL | http://www.aes.org/e-lib/browse.cfm?elib=19638 |
Related Public URLs | http://www.aes.org/conferences/2018/spatial/ |
Additional Information | Event Type : Conference |
You might also like
Cloud-based AI for automatic audio production for personalized immersive XR experiences
(2022)
Journal Article
Loudness differences for Voice-over-Voice audio in TV and streaming
(2020)
Journal Article
Downloadable Citations
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search