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  5. AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
 
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AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio

Author(s)
Narbutt, Miroslaw  
Skoglund, Jan  
Allen, Andrew  
Chinen, Michael  
Barry, Dan  
Hines, Andrew  
Uri
http://hdl.handle.net/10197/11947
Date Issued
2020-05-03
Date Available
2021-02-16T15:09:15Z
Abstract
Spatial audio is essential for creating a sense of immersion in virtual environments. Efficient encoding methods are required to deliver spatial audio over networks without compromising Quality of Service (QoS). Streaming service providers such as YouTube typically transcode content into various bit rates and need a perceptually relevant audio quality metric to monitor users’ perceived quality and spatial localization accuracy. The aim of the paper is two-fold. First, it is to investigate the effect of Opus codec compression on the quality of spatial audio as perceived by listeners using subjective listening tests. Secondly, it is to introduce AMBIQUAL, a full reference objective metric for spatial audio quality, which derives both listening quality and localization accuracy metrics directly from the B-format Ambisonic audio. We compare AMBIQUAL quality predictions with subjective quality assessments across a variety of audio samples which have been compressed using the Opus 1.2 codec at various bit rates. Listening quality and localization accuracy of first and third-order Ambisonics were evaluated. Several fixed and dynamic audio sources (single and multiple) were used to evaluate localization accuracy. Results show good correlation regarding listening quality and localization accuracy between objective quality scores using AMBIQUAL and subjective scores obtained during listening tests.
Sponsorship
European Commission - European Regional Development Fund
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Google LLC
Type of Material
Journal Article
Publisher
MDPI
Journal
Applied Science
Volume
10
Issue
9
Copyright (Published Version)
2020 the Authors
Subjects

Machine learning & st...

Virtual reality

Ambisonics

Audio coding

Audio compression

Opus codec

MUSHRA

Audio quality

QoE

DOI
10.3390/app10093188
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by/3.0/ie/
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insight_publication.pdf

Size

3.33 MB

Format

Adobe PDF

Checksum (MD5)

741fe9940d88d0f6dc0520c886d2e2b5

Owning collection
Insight Research Collection
Mapped collections
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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