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 language | English |
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Title of host publication | 2020 International Joint Conference on Neural Networks (IJCNN) |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Number of pages | 7 |
ISBN (Electronic) | 9781728169262 |
DOIs | |
Publication status | Published - 28 Sept 2020 |
Event | 2020 International Joint Conference on Neural Networks - Online Duration: 19 Jul 2020 → 24 Jul 2020 https://research.com/conference/ijcnn-2020 (Link to conference website) |
Publication series
Name | Proceedings of the International Joint Conference on Neural Networks |
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ISSN (Electronic) | 2161-4407 |
Conference
Conference | 2020 International Joint Conference on Neural Networks |
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Abbreviated title | IJCNN 2020 |
Period | 19/07/20 → 24/07/20 |
Internet address |
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Keywords
- CNN
- deep learning
- MOS
- NB
- SWB
- voice quality
- VoLTE
ASJC Scopus subject areas
- Software
- Artificial Intelligence