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QoE Modeling for Voice over IP: Simplified E-model Enhancement Utilizing the Subjective MOS Prediction Model: A Case of G.729 and Thai Users

  • Therdpong DaengsiEmail author
  • Pongpisit Wuttidittachotti
Article
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Abstract

This research proposes an enhanced measurement method for VoIP quality assessment which provides an improvement to accuracy and reliability. To improve the objective measurement tool called the simplified E-model for the selected codec, G.729, it has been enhanced by utilizing a model of Mean Opinion Score (MOS), called a subjective MOS prediction model, based on native Thai users who use the Thai-tonal language. Then, the different results from the simplified E-model and subjective MOS prediction model were used to create the Bias function, before adding to the simplified E-model. Finally, it has been found that the outputs from the enhanced simplified E-model for the G.729 codec show better accuracy when compared to the original simplified E-model, specially, after the enhanced model has been evaluated with 4 test sets. The major contribution of this enhancement is that errors are reduced by 58.87% when compared to the generic simplified E-model. That means the enhanced simplified E-model as proposed in this study can provide improvement beyond the original simplified one significantly.

Keywords

VoIP Quality of experience Mean Opinion Score Subjective test Codec Packet loss Packet delay 

Notes

Acknowledgements

Thank you to Rajamangala University of Technology Phra Nakhon and King Mongkut’s University of Technology North Bangkok for supporting this research. Thank you to Ms. Nalakkhana Khitmoh and Mr. Jakkapong Polpong for the data set of subjective and objective results associated with G.729 codec. Thank you to Mr. Montri Rungruangthum for assistance with parts of the calculations. Lastly, thank you very much to Mr. Gary Sherriff for editing.

References

  1. 1.
    Karapantazis, S., Pavlidou, F.-N.: VoIP: a comprehensive survey on a promising technology. Comput. Netw. 53(12), 2050–2090 (2009)CrossRefGoogle Scholar
  2. 2.
    Daengsi, T., Wuttidittachotti, P.: QoE modeling: a simplified E-model enhancement using subjective MOS estimation model. In: ICUFN 2015, pp. 386–390Google Scholar
  3. 3.
    Saleha, S., Shahb, Z., Baiga, A.: Improving QoS of IPTV and VoIP over IEEE 802.11n. Comput. Electr. Eng. 43, 92–111 (2015)CrossRefGoogle Scholar
  4. 4.
    Gonia, K.: Latency and QoS for voice over IP. https://www.sans.org/reading-room/whitepapers/voip/latency-qos-voice-ip-1349. Accessed 19 Oct 2016 (2004)
  5. 5.
    De Pessemier, T., Stevens, I., De Marez, L., Martens, L., Joseph, W.: Quality assessment and usage behavior of a mobile voice-over-IP service. Telecommun. Syst. 61, 417–432 (2016)CrossRefGoogle Scholar
  6. 6.
    Assem, H., Malone, D., Dunne, J., O’Sullivan, P.: Monitoring VoIP call quality using improved simplified E-model. In: ICNC 2013, pp. 927–931Google Scholar
  7. 7.
    Jiang, C., Huang, P.: Research of monitoring VoIP voice QoS. In: ICICIS 2011, pp. 499–502Google Scholar
  8. 8.
    Goudarzi, M., Sun L., Ifeachor E.: Performance analysis and comparison of PESQ and 3SQM in live 3G mobile networks. In: MESAQIN (2009)Google Scholar
  9. 9.
    Johannesson, N.O.: The ETSI computation model: a tool for transmission planning of telephone networks. IEEE Commun. Mag. 35, 70–79 (1997)CrossRefGoogle Scholar
  10. 10.
    ITU-T.: ITU-T recommendation G.107, The E-model: a computational model for use in transmission planning (2011)Google Scholar
  11. 11.
    ITU-T.: ITU-T Recommendation G.107, The E-model: a computational model for use in transmission planning (2008)Google Scholar
  12. 12.
    Tantibundhit, C., Onsuwan, C.: Speech intelligibility tests and analysis of confusions and perceptual representations of Thai initial consonants. Speech Commun. 72, 109–125 (2015)CrossRefGoogle Scholar
  13. 13.
    Daengsi, T., Khitmoh, N., Wuttidittachotti, P.: VoIP quality measurement: subjective VoIP quality estimation model for G.711 and G.729 based on native Thai users. Multimed. Syst. 22(5), 575–586 (2016).  https://doi.org/10.1007/s00530-015-0468-3 CrossRefGoogle Scholar
  14. 14.
    Wuttidittachotti, P., Daengsi, T.: VoIP-quality of experience modeling: E-model and simplified E-model enhancement using bias factor. Multimed. Tools Appl. 76(6), 8329–8354 (2017).  https://doi.org/10.1007/s11042-016-3389-z CrossRefGoogle Scholar
  15. 15.
    ITU-T.: Question 7/12—methods, tools and test plans for the subjective assessment of speech, audio and audiovisual quality interactions. http://www.itu.int/ITU-T/studygroups/com12/sg12-q7.html. Accessed 18 Dec 2018
  16. 16.
    ITU-T.: ITU-T work programme. http://www.itu.int/ITU-T/workprog/wp_item.aspx?isn=8878. Accessed 18 Dec 2018
  17. 17.
    Pomy, J.: Overview of ITU-T Study Group 12 Activities. Accessed 18 Dec 2018Google Scholar
  18. 18.
    Javed, Y., Baig, A., Maqbool, M.: Enhanced quality of service support for triple play services in IEEE 802.11 WLANs. EURASIP. J. Wirel. Commun. 15, 9 (2015).  https://doi.org/10.1186/s13638-014-0233-x Google Scholar
  19. 19.
    ITU-T.: ITU-T recommendation G.729, coding of speech at 8 kbit/s using conjugate-structure algebraic-code-excited linear prediction (CS-ACELP) (2012)Google Scholar
  20. 20.
    Cisco.: Voice Over IP—Per Call Bandwidth Consumption. http://www.cisco.com/c/en/us/support/docs/voice/voice-quality/7934-bwidth-consume.html. Accessed 18 Dec 2018
  21. 21.
    ITU-T.: ITU-T Recommendation P.800.1, Mean Opinion Score (MOS) terminology (2016)Google Scholar
  22. 22.
    ITU-T.: ITU-T Recommendation P.862.3, application guide for objective quality measurement based on Recommendations P.862, P.862.1 and P.862.2 (2007)Google Scholar
  23. 23.
    Szigeti, T., Hattingh, C., Barton, R., Briley, K.: Quality of Service Design Overview. http://www.cisco.com/c/en/us/td/docs/solutions/Enterprise/WAN_and_MAN/QoS_SRND/QoS-SRND-Book/QoSIntro.html. Accessed 18 Dec 2018
  24. 24.
    Boutremans, C., Iannaccone, G., Diot, C.: Impact of link failures on VoIP performance. In: NOSSDAV 2002, pp. 63–71Google Scholar
  25. 25.
    Markopoulou, A., Iannaccone, G., Bhattacharyya, S., Chuah, C.-N., Diot, C.: Characterization of failures in an IP backbone. In: IEEE INFOCOM 2004, pp. 2307–2317Google Scholar
  26. 26.
    Jelassi, S., Rubino, G.: A study of artificial speech quality assessors of VoIP calls subject to limited bursty packet losses. EURASIP J. Image. Video (2011).  https://doi.org/10.1186/1687-5281-2011-9 Google Scholar
  27. 27.
    Avaya Labs.: Avaya IP Voice Quality Network Requirement. https://downloads.avaya.com/elmodocs2/audio_quality/lb1500-02.pdf. Accessed 18 Dec 2018
  28. 28.
    ITU-T.: ITU-T Recommendation G.114, One-way transmission time (2003)Google Scholar
  29. 29.
    Suarez, F.J., Garcia, A., Granda, J.C., Garcia, D.F., Nuno, P.: Assessing the QoE in video services over lossy networks. J. Netw. Syst. Manag. 24, 16–139 (2016)CrossRefGoogle Scholar
  30. 30.
    Ickin, S., Fiedler, M., Wac, K., Arlos, P., Temiz, C., Mkocha, K.: VLQoE: video QoE instrumentation on the smartphone. Multimed. Tools Appl. 74(2), 381–411 (2015).  https://doi.org/10.1007/s11042-014-1919-0 CrossRefGoogle Scholar
  31. 31.
    Hoßfeld, T., Seufert, M., Sieber, C., Zinner, T., Tran-Gia, P.: Identifying QoE optimal adaptation of HTTP adaptive streaming based on subjective studies. Comput. Netw. 81, 320–332 (2015)CrossRefGoogle Scholar
  32. 32.
    Li, M., Lee, C.-Y.: A cost-effective and real-time QoE evaluation method for multimedia streaming services. Telecommun. Syst. 59, 317–327 (2015)CrossRefGoogle Scholar
  33. 33.
    Stojanovic, M.D., Vukasinovic, M.M., Djogatovic, V.M. R.: Approaches to quality of experience management in the future internet. In: TELSIKS 2015, pp. 281–288Google Scholar
  34. 34.
    ITU-T.: ITU-T Recommendation P.800, Methods for subjective determination of transmission quality (1996)Google Scholar
  35. 35.
    ITU-T.: ITU-T Recommendation P.805, Subjective evaluation of conversational quality (2007)Google Scholar
  36. 36.
    Ding, L., Lin, Z., Radwan, A., El-Hennawey, M.S., Goubran, R.A.: Non-intrusive single-ended speech quality assessment in VoIP. Speech Commun. 49, 477–489 (2007)CrossRefGoogle Scholar
  37. 37.
    Schmitt, A., Ultes, S.: Interaction quality: assessing the quality of ongoing spoken dialog interaction by experts—and how it relates to user satisfaction. Speech Commun. 74, 12–36 (2015)CrossRefGoogle Scholar
  38. 38.
    Daengsi, T., Wutiwiwatchai, C., Preechayasomboon, A., Sukparungsee, S.: IP telephony: comparison of subjective assessment methods for voice quality evaluation. Walailak J. Sci. Technol. 11(2), 87–92 (2014)Google Scholar
  39. 39.
    Daengsi, T., Wuttidittachotti, P., Preechayasomboon, A., Wutiwiwatchai, C., Sukparungsee, S.: VoIP quality of experience: a proposed subjective MOS estimation model based-on Thai users. In: ICUFN 2013, pp. 407–412Google Scholar
  40. 40.
    Khitmah, N., Daengsi, T., Wuttidittachotti, P.: A subjective—VoIP quality estimation model for G.729 based on native Thai users. In: ICACT 2014, pp. 48–53Google Scholar
  41. 41.
    Sun, L.: Speech Quality Prediction for Voice over Internet Protocol Networks. Ph.D. thesis, University of Plymouth (2008)Google Scholar
  42. 42.
    IEEEXplore.: Displaying results 1–25 of 179 for (((E-model VoIP Quality) OR E-model voice Quality) OR E-model speech Quality) and refined by Year: 2001–2018. https://ieeexplore.ieee.org/search/searchresult.jsp?queryText=(((E-model%20VoIP%20Quality)%20OR%20E-model%20voice%20Quality)%20OR%20E-model%20speech%20Quality)&ranges=2001_2018_Year&matchBoolean=true&searchField=Search_All. Accessed 18 Dec 2018
  43. 43.
    Ahmad, A., Floris, A., Atzori, L.: QoE-centric service delivery: a collaborative approach among OTTs and ISPs. Comput. Netw. 110, 168–179 (2016)CrossRefGoogle Scholar
  44. 44.
    ITU-T.: ITU-T recommendation G.113, transmission impairments due to speech processing (2007)Google Scholar
  45. 45.
    Zhou, X., Muller, F., Kooij, R.E., Mieghem, P.V.: Estimation of voice over IP quality in the Netherlands. In: IPS-MoMe 2006, Salzburg, Austria, 27–28 Feb 2006Google Scholar
  46. 46.
    Sun, L., Ifeachor, E.: Voice quality prediction models and their application in VoIP networks. IEEE Trans. Multimed. 8(4), 809–820 (2006)CrossRefGoogle Scholar
  47. 47.
    Daengsi, T., Wuttidittachotti, P.: VoIP quality measurement: enhanced E-model using bias factor. In: IEEE GLOBECOM 2013, pp. 1329–1334Google Scholar
  48. 48.
    Ding, L., Goubran, R. A.: Speech quality prediction in VoIP using the extended E-model. In: IEEE GLOBECOM 2003, pp. 3974–3978Google Scholar
  49. 49.
    Takahashi, A., Kurashima, A., Yoshino, H.: Objective assessment methodology for estimating conversational quality in VoIP. IEEE Trans. Speech Audio Process. 14(6), 1983–1993 (2006)Google Scholar
  50. 50.
    Ren, J., Zhang, H., Zhu, Y., Gao, C.: Assessment of effects of different language in VOIP. In: ICALIP 2008, pp. 1624–1628Google Scholar
  51. 51.
    Ren, J., Zhang, C., Huang, W., Mao, D.: Enhancement to E-model on standard deviation of packet delay. In: ICIS 2010, pp. 256–259Google Scholar
  52. 52.
    Voznak, M.: E-model modification for case of cascade codecs arrangement. Int. J. Math. Models Methods Appl. Sci. 6(8), 1301–1309 (2011)Google Scholar
  53. 53.
    Goudarzi, M., Sun, L., Ifeachor, E.: Modelling speech quality for NB and WB SILK codec for VoIP applications. In: NGMAST 2011, pp. 42–47Google Scholar
  54. 54.
    Adel, M., Assem, H., Jennings, B., Malone, D., Dunne, J., O’Sullivan, P.: Improved E-model for monitoring quality of multi-party VoIP communications. In: IEEE Globecom Workshops 2013, pp. 1180–1185Google Scholar
  55. 55.
    Giri, M.K., Tiwari, G.: Enhancing voice quality through improved E-model. Int. J. Eng. Technol. Comput. Res. 3(4), 96–104 (2015)Google Scholar
  56. 56.
    Triyason, T., Kanthamanon, P.: E-model modification for multi-languages over IP. Elektron. Elektrotech. 21(1), 82–87 (2015)Google Scholar
  57. 57.
    Triyason, T., Kanthamanon, P.: E-model parameters estimation for VoIP with non-ITU codec speech quality prediction. In: IC2IT 2016, pp. 309–318Google Scholar
  58. 58.
    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(15), 16163–16187 (2016).  https://doi.org/10.1007/s11042-016-3901-5 CrossRefGoogle Scholar
  59. 59.
    Prommate, N., Wuttidittachotti, P., Daengsi, T.: Traffic classification and identification for LINE and Facebook messenger application. In: NCCIT 2018, pp. 624–629 (in Thai) Google Scholar
  60. 60.
    Dalmazo, B.L., Vilela, J.P., Curado, M.: Performance analysis of network traffic predictors in the cloud. J. Netw. Syst. Manag. 25(2), 290–320 (2017).  https://doi.org/10.1007/s10922-016-9392-x CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Sustainable Industrial Management Engineering, Faculty of EngineeringRajamangala University of Technology Phra NakhonBangkokThailand
  2. 2.Department of Data Communication and Networking, Faculty of Information TechnologyKing Mongkut’s University of Technology North BangkokBangkokThailand

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