Voice over LTE Quality Evaluation Using Convolutional Neural Networks
| Autor: | Ayyaz-Ul-Haq Qureshi, Hadi Larijani, Thomas Gorman |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Public switched telephone network
Computer science media_common.quotation_subject Speech recognition 020206 networking & telecommunications 02 engineering and technology Voice over Network emulation Convolutional neural network Narrowband 0202 electrical engineering electronic engineering information engineering Codec 020201 artificial intelligence & image processing Quality (business) media_common |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn48605.2020.9207540 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|