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

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Title: AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
Authors: Narbutt, MiroslawSkoglund, JanAllen, AndrewChinen, MichaelBarry, DanHines, Andrew
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Date: 3-May-2020
Online since: 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.
Funding Details: European Commission - European Regional Development Fund
Science Foundation Ireland
Funding Details: 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
Keywords: Machine learning & statisticsVirtual realityAmbisonicsAudio codingAudio compressionOpus codecMUSHRAAudio qualityQoE
DOI: 10.3390/app10093188
Language: en
Status of Item: Peer reviewed
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Appears in Collections:Computer Science Research Collection
Insight Research Collection

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