Voice over LTE quality evaluation using convolutional neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Modern packet-switched networks are increasingly capable of offering high-quality voice services such as Voice over LTE (VoLTE) which have the potential to surpass the Public Switched Telephone Network (PSTN) in terms of quality. To ensure this development is sustained, it is important that suitable quality evaluation methods exist in order to help measure and identify the effect of network impairments on voice quality. In this paper, a single-ended, objective voice quality evaluation model is proposed, utilizing a Convolutional Neural Network with regression-style output (CQCNN) to predict mean opinion scores (MOS) of speech samples impaired by a VoLTE network emulation. The results of this experiment suggest that a deep-learning approach using CNNs is highly successful at predicting MOS values for both narrowband (NB) and super-wideband (SWB) samples with an accuracy of 91.91% and 82.50% respectively.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks (IJCNN)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781728169262
DOIs
Publication statusPublished - 28 Sep 2020
Event2020 International Joint Conference on Neural Networks - Online
Duration: 19 Jul 202024 Jul 2020
https://research.com/conference/ijcnn-2020 (Link to conference website)

Publication series

NameProceedings of the International Joint Conference on Neural Networks
ISSN (Electronic)2161-4407

Conference

Conference2020 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2020
Period19/07/2024/07/20
Internet address

Keywords

  • CNN
  • deep learning
  • MOS
  • NB
  • SWB
  • voice quality
  • VoLTE

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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