ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric

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Title: ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric
Authors: Chinen, MichaelLim, Felicia S. C.Skoglund, JanGureev, NikitaO'Gorman, FeargusHines, Andrew
Permanent link: http://hdl.handle.net/10197/12214
Date: 28-May-2020
Online since: 2021-05-27T09:28:39Z
Abstract: Estimation of perceptual quality in audio and speech is possible using a variety of methods. The combined v3 release of ViSQOL and ViSQOLAudio (for speech and audio, respectively,) provides improvements upon previous versions, in terms of both design and usage. As an open source C++ library or binary with permissive licensing, ViSQOL can now be deployed beyond the research context into production usage. The feedback from internal production teams at Google has helped to improve this new release, and serves to show cases where it is most applicable, as well as to highlight limitations. The new model is benchmarked against real-world data for evaluation purposes. The trends and direction of future work is discussed.
Funding Details: European Commission - European Regional Development Fund
Science Foundation Ireland
Funding Details: Insight Research Centre
Google Chrome University Program
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2020 IEEE
Keywords: Machine learning & statisticsPerceptual audio quality assessmentMean opinion score estimationViSQOLAudioViSQOLPESQPOLQAPEAQPEMO-Q
DOI: 10.1109/QoMEX48832.2020.9123150
Other versions: https://qomex2020.ie/
Language: en
Status of Item: Peer reviewed
Is part of: 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)
Conference Details: The 12th International Conference on Quality of Multimedia Experience (QoMEX), Athlone, Ireland (held online due to coronavirus outbreak), 26-28 May 2020
ISBN: 978-1-7281-5965-2
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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