Research and application of adaptive algorithm for 5G voice quality evaluation
MOS (mean opinion score) is usually used to evaluate voice quality in the industry.It can objectively and fairly reflect the user’s voice service perception.It is difficult and costly to obtain data by road test, so a trained supervised learning model is usually used to predict the MOS score.However...
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| Format: | Article |
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Beijing Xintong Media Co., Ltd
2023-11-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023249/ |
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| _version_ | 1841533634910093312 |
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| author | Yuxiang ZHAO Yaxin JI Li YU Tianyi ZHOU Hang ZHOU |
| author_facet | Yuxiang ZHAO Yaxin JI Li YU Tianyi ZHOU Hang ZHOU |
| author_sort | Yuxiang ZHAO |
| collection | DOAJ |
| description | MOS (mean opinion score) is usually used to evaluate voice quality in the industry.It can objectively and fairly reflect the user’s voice service perception.It is difficult and costly to obtain data by road test, so a trained supervised learning model is usually used to predict the MOS score.However, the operator voice data has the characteristics of low percentage of MOS low score data and time sequence change, which affects the accuracy and generalization of the model prediction.Based on the study of existing data acquisition systems and machine learning algorithms of operators, an adaptive algorithm for MOS evaluation of 5G speech quality was proposed.Firstly, POLQA algorithm test equipment based on full parameter evaluation obtained training data to ensure the accuracy of training samples.Secondly, by means of data enhancement, the difficulty of acquiring poor quality samples was solved.Finally, based on the adaptive algorithm selection, the optimal MOS prediction model could be selected periodically and dynamically according to the timing changes of data features, so as to achieve large-scale and intelligent evaluation of 5G voice quality. |
| format | Article |
| id | doaj-art-6535e067a6364f84af986da216e23e70 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-11-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-6535e067a6364f84af986da216e23e702025-01-15T02:57:59ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-11-013915316359559634Research and application of adaptive algorithm for 5G voice quality evaluationYuxiang ZHAOYaxin JILi YUTianyi ZHOUHang ZHOUMOS (mean opinion score) is usually used to evaluate voice quality in the industry.It can objectively and fairly reflect the user’s voice service perception.It is difficult and costly to obtain data by road test, so a trained supervised learning model is usually used to predict the MOS score.However, the operator voice data has the characteristics of low percentage of MOS low score data and time sequence change, which affects the accuracy and generalization of the model prediction.Based on the study of existing data acquisition systems and machine learning algorithms of operators, an adaptive algorithm for MOS evaluation of 5G speech quality was proposed.Firstly, POLQA algorithm test equipment based on full parameter evaluation obtained training data to ensure the accuracy of training samples.Secondly, by means of data enhancement, the difficulty of acquiring poor quality samples was solved.Finally, based on the adaptive algorithm selection, the optimal MOS prediction model could be selected periodically and dynamically according to the timing changes of data features, so as to achieve large-scale and intelligent evaluation of 5G voice quality.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023249/5G voice qualityMOSmachine learningadaptive |
| spellingShingle | Yuxiang ZHAO Yaxin JI Li YU Tianyi ZHOU Hang ZHOU Research and application of adaptive algorithm for 5G voice quality evaluation Dianxin kexue 5G voice quality MOS machine learning adaptive |
| title | Research and application of adaptive algorithm for 5G voice quality evaluation |
| title_full | Research and application of adaptive algorithm for 5G voice quality evaluation |
| title_fullStr | Research and application of adaptive algorithm for 5G voice quality evaluation |
| title_full_unstemmed | Research and application of adaptive algorithm for 5G voice quality evaluation |
| title_short | Research and application of adaptive algorithm for 5G voice quality evaluation |
| title_sort | research and application of adaptive algorithm for 5g voice quality evaluation |
| topic | 5G voice quality MOS machine learning adaptive |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023249/ |
| work_keys_str_mv | AT yuxiangzhao researchandapplicationofadaptivealgorithmfor5gvoicequalityevaluation AT yaxinji researchandapplicationofadaptivealgorithmfor5gvoicequalityevaluation AT liyu researchandapplicationofadaptivealgorithmfor5gvoicequalityevaluation AT tianyizhou researchandapplicationofadaptivealgorithmfor5gvoicequalityevaluation AT hangzhou researchandapplicationofadaptivealgorithmfor5gvoicequalityevaluation |