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An End-to-End Listening Test Platform

Author(s)
Barry, Dan  
Zhang, Qijian  
Sun, Pheobe Wenyi  
Hines, Andrew  
Uri
http://hdl.handle.net/10197/25884
Date Issued
2020-09-23
Date Available
2024-05-08T14:14:31Z
Abstract
Subjective listening tests are routinely conducted by academic researchers and industry professionals to assess the quality of various speech and audio processing algorithms. Some examples include low bit rate audio encoding, background noise reduction, text to speech synthesis or any type of audio enhancement or transformation one might think of. Listening tests often take place in controlled environments for the sake of consistency, but in many cases, listening tests could be undertaken remotely using a suitable web interface. The requirement for a web-based listening test platform has become more apparent in light of the recent implications of COVID-19. Despite the work of several projects in the past, there is no fully hosted listening test platform which allows for easy test creation, deployment and data collection. In this demonstration paper we present a fully functional end-to-end listening test platform which allows a user to create and share MUSHRA, ACR and A/B tests within minutes. Collected data can then be downloaded in CSV form.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Conference Publication
Subjects

Machine learning & st...

MUSHRA

ACR

P800

A/B listening test

Software

Web versions
https://signalprocessingsociety.org/blog/mmsp-2020-2020-ieee-22nd-international-workshop-multimedia-signal-processing
Language
English
Status of Item
Peer reviewed
Conference Details
The IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), Tempere, Finland, 21-23 September 2020
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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Thumbnail Image
Name

An_End_to_End_Listening_Test_Platform (1).pdf

Size

64.75 KB

Format

Adobe PDF

Checksum (MD5)

3b672c76e335bc7032d9adac66207d5a

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/.
All other content is subject to copyright.

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