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Subjective MOS model and simplified E-model enhancement for Skype associated with packet loss effects: a case using conversation-like tests with Thai users

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Abstract

This paper proposes two mathematical models that can be used to estimate VoIP quality from Skype, which is one of the most popular VoIP applications. The first model is simple, it has been developed using data from the informal interview tests called Conversation-like tests, referring to packet loss of 0 %, 5 %, 10 %, …, and 30 %. The tests have been conducted with Skype using a non ITU-T’s codec called SILK via the Internet with over 180 native Thai participants, while packet loss effects were generated using a network emulation tool. The second model is called the Enhanced Simplified E-model, this has been developed by adding the Thai Bias factor into a generic Simplified E-model, which calculates by subtracting the subjective results from the computed results using the Simplified E-model formula. After obtaining the models, they were evaluated with the Test set from 36 native Thai participants (different from the other group of participants) using Mean Absolute Percentage Error technique (MAPE). It has been found that VoIP quality measurement performance of both models are classified as excellent and provide higher reliability and accuracy than the Simplified E-model. Subjective MOS model and Enhanced Simplified E-model error reduction compared to the simplified one was at about 21.9 % and 21.2 % respectively, which is the major contribution of this work.

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Acknowledgments

Thank you to all reviewers for very useful comments. Thank you to all participants, staff and lecturers at KMUTNB, who were involved. Particularly, thanks to Mr. Montri Rungruangthum, a master degree student in Faculty of Information Technology, KMUTNB, for conducting tests and data collection. Thanks Mr. Gary Sherriff for English editing.

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Wuttidittachotti, P., Daengsi, T. Subjective MOS model and simplified E-model enhancement for Skype associated with packet loss effects: a case using conversation-like tests with Thai users . Multimed Tools Appl 76, 16163–16187 (2017). https://doi.org/10.1007/s11042-016-3901-5

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