| Full text | |
| Author(s): |
da Silva, Marielle Jordane
[1]
;
Melgarejo, Dick Carrillo
[2]
;
Rosa, Renata Lopes
[1]
;
Rodriguez, Demostenes Zegarra
[1]
Total Authors: 4
|
| Affiliation: | [1] Univ Fed Lavras, Dept Comp Sci, Lavras, MG - Brazil
[2] LUT Sch Energy Syst, Lappeenranta - Finland
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | JOURNAL OF COMMUNICATIONS SOFTWARE AND SYSTEMS; v. 16, n. 1, p. 75-84, MAR 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Current implementations of 5G networks consider higher frequency range of operation than previous telecommunication networks, and it is possible to offer higher data rates for different applications. On the other hand, atmospheric phenomena could have a more negative impact on the transmission quality. Thus, the study of the transmitted signal quality at high frequencies is relevant to guaranty the user ` s quality of experience. In this research, the recommendations ITU-R P.8383 and ITU-R P.676-11 are implemented in a network scenario, which are methodologies to estimate the signal degradations originated by rainfall and atmospheric gases, respectively. Thus, speech signals are encoded by the Adaptive Multi-RateWideband (AMR-WB) codec, transmitted and the perceptual speech quality is evaluated using the algorithm described in ITU-T Rec. P.863, mostly known as POLQA. In this work, a novel non-intrusive speech quality classifier that considers atmospheric phenomena is proposed. This classifier is based on Deep Belief Networks (DBN) that uses Support Vector Machine (SVM) with radial basis function kernel (RBF-SVM) as classifier, to identify five predefined speech quality classes. Experimental results show that the proposed speech quality classifier reached an accuracy between 92% and 95% for each quality class overcoming the results obtained by the sole non-intrusive standard described in ITU-T Recommendation P.563. Furthermore, subjective tests are carried out to validate the proposed classifier performance, and it reached an accuracy of 94.8%. (AU) | |
| FAPESP's process: | 15/24496-0 - Evaluation of the service of communication operators using the voice Quality Index |
| Grantee: | Demostenes Zegarra Rodriguez |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 18/26455-8 - Audio-Visual Speech Processing by Machine Learning |
| Grantee: | Miguel Arjona Ramírez |
| Support Opportunities: | Regular Research Grants |