Skip to main content
SpringerLink
Log in

VoIP-quality of experience modeling: E-model and simplified E-model enhancement using bias factor

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The E-model is a non-intrusive measurement method that many researchers have applied to the study of VoIP quality measurement. While the Simplified E-model is a modified version from the original, it can still be used as an alternative solution. Nevertheless, it has been found that the E-model and the Simplified E-model still require further improvement. Therefore, to enhance the original E-model, this paper proposes a new factor. Moreover, the Simplified E-model has also been enhanced by the same approach. Based-on the Thai environment, the new factor called Thai Bias factor, can be computed by subtracting the subjective test results using conversation tests with native Thai users from the objective test results using an E-model tool and the Simplified E-model calculation. Of course, both E-mode tests and conversation tests were conducted with the same VoIP system and test scenarios. The Enhanced E-model and the Simplified E-model using the Thai Bias factor were then evaluated by comparing the test set from other groups of native Thai users. After evaluation of the improved models, it has been found that the Enhanced E-model and the enhanced Simplified E-model can gain higher confidence. The Enhanced E-model delivers improved accuracy and reliability at approximately more than 20 % when compared to an available E-model tool, while the Enhanced Simplified E-model delivers improved performance at approximately more than 46 % when compared to Simplified E-model calculation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Al-Akhras M, Zedan H, John R, ALMomani I (2009) Non-intrusive speech quality prediction in VoIP networks using a neural network approach. Neurocomputing 72(10–12):2595–2608. doi:10.1016/j.neucom.2008.10.019

    Article  Google Scholar 

  2. Altbach PG (2004) The past and future of Asian universities. In: Altbach PG, Umakoshi T (eds) Asian universities: historical perspectives and contemporary challenges. The Johns Hopkins University Press, Baltimore London, pp 13–32

    Google Scholar 

  3. Assem H, Malone D, Dunne J, O’Sullivan P (2013) Monitoring VoIP call quality using improved simplified E-model. In: Proceedings of ICNC 2013, San Diego, pp. 927–931

  4. Batteram H, Damm G, Mukhopadhyay A, Philippart L, Odysseos R, Urrutia-Valdés C (2010) Delivering quality of experience in multimedia networks. Bell Labs Tech J 15(1):175–194. doi:10.1002/bltj.v15:1

    Article  Google Scholar 

  5. Boutremans C, Iannaccone G, Diot C (2002) Impact of link failures on VoIP performance. In: Proceedings of NOSSDAV 2002, Miami Beach, pp. 63–71

  6. Cai Z, Kitawaki N, Yamada T, Makino S (2010) Comparison of MOS evaluation characteristics for Chinese, Japanese, and English in IP telephony. In: Proceedings of IUCS, Beijing, pp. 1–4

  7. Cote N (2011) Integral and diagnostic Instrusive prediction of speech quality. Springer, Berlin Heidelberg

    Book  Google Scholar 

  8. Daengsi T (2012) VoIP Quality Measurement: Recommendation of MOS and Enhanced Objective Measurement Method for Standard Thai Spoken Language. Dissertation, King Mongkut’s University of Technology North Bangkok

  9. Daengsi T, Preechayasomboon A, Sukparungsee S, Chootrakool P, Wutiwiwatchai C (2010) The Development of a Thai Speech Set for Telephonometry. In: Proceedings of O-COCOSDA2010, Kathmandu, Paper 53

  10. Daengsi T, Sukparungsee S, Wutiwiwatchai C, Preechayasomboon A (2012) Comparison of percetual voice quality of VoIP provided by G.711 and G.729 using conversation-opinion tests. Int J Comput Internet Manag 20(1):21–26

    Google Scholar 

  11. Daengsi T, Wuttidittachotti T (2013) VoIP Quality Measurement: Enhanced E-model Using Bias Factor. In: Proceedings of IEEE GLOBECOM 2013, Atlanta, pp. 1329–1334

  12. Daengsi T, Wuttidittachotti P (2015) QoE Modeling: A Simplified E-model Enhancement Using Subjective MOS Estimation odel. In: Proceedings of ICUFN 2015, Sapporo, pp. 386–390.

  13. De Pessemier T, Stevens I, De Marez L, Martens L, Joseph W (2014) Analysis of the quality of experience of a commercial voice-over-IP service. Multimed Tools Appl. doi:10.1007/s11042-014-1895-4

    Google Scholar 

  14. De Rango F, Tropea M, Fazio P, Marano S (2006) Overview on VoIP: subjective and objective measurement methods. Int J Comput Sci Netw Secur 6(1B):140–153

    Google Scholar 

  15. Ding L, Goubran R A (2003) Speech quality prediction in VoIP using the extended E-model. In: Proceedings of IEEE GLOBECOM, San Francisco, pp. 3974–3978

  16. Ding L, Lin Z, Radwan A, El-Hennawey MS, Goubran RA (2007) Non-intrusive single-ended speech quality assessment in VoIP. Speech Commun 49(6):477–489

    Article  Google Scholar 

  17. Fluke Corporation (2005) Quality Management: Troubleshooting Techniques for Voice over IP. Available at http://www.tequipment.net/pdf/FlukeNetworks/ApplicationNote-QualityManagement-TroubleshootingTechniquesForVoiceOverIP.pdf. Accessed May 2015

  18. Gandour J, Wong D, Hutchins G (1998) Pitch processing in the human brain is influenced by language experience. NeuroReport 9:2115–2119

    Article  Google Scholar 

  19. Goudarzi M (2008) Evaluation of Voice Quality in 3G Mobile Networks. Thesis, University of Plymouth

  20. Hiwasaki Y, Ohmuro H (2009) ITU-T G.711.1: extending G.711 to higher-quality wideband speech. IEEE Commun Mag 47(10):110–116. doi:10.1109/MCOM.2009.5273817

    Article  Google Scholar 

  21. Huang Y-T, Neoh C-A, Lin S-Y, Shi H-Y (2013) Comparisons of Prediction Models of Myofascial Pain Control after Dry Needling: A Prospective Study. Evidence-Based Complementary and Alternative Medicine 10.1155/2013/478202

  22. Indepth: Packet Loss Burstiness. Available at http://www.voiptroubleshooter.com/indepth/burstloss.html. Accessed May 2015

  23. ITU-T (1996) ITU-T Recommendation P.800: Methods for subjective determination of transmission quality

  24. ITU-T (1996) ITU-T Recommendation P.800.1: Mean Opinion Score (MOS) terminology

  25. ITU-T (2001) ITU-T Recommendation P.862: Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end speech quality assessment of narrow-band telephone

  26. ITU-T (2003) ITU-T Recommendation G.114: One-way transmission time

  27. ITU-T (2007) ITU-T Recommendation P.805: Subjective evaluation of conversational quality

  28. ITU-T (2009) Question 7/12 – Methods, tools and test plans for the subjective assessment of speech, audio and audiovisual quality interactions. Available at http://www.itu.int/ITU-T/studygroups/com12/sg12-q7.html. Accessed May 2015

  29. ITU-T (2009) ITU-T Recommendation G.107: The E-model: a computational model for use in transmission planning

  30. ITU-T (2011) ITU-T Recommendation G.107: The E-model: a computational model for use in transmission planning

  31. ITU-T (2015) Accessibility and Standardization. Available at http:// http://www.itu.int/en/ITU-T/studygroups/com16/accessibility/Pages/default.aspx. Accessed October 2015

  32. ITU-T Test Signals for Telecommunication Systems (2015) Test Vectors Associated to Rec. ITU-T P.501. Available at http://www.itu.int/net/itu-t/sigdb/genaudio/AudioForm-g.aspx?val=10000501. Accessed May 2015

  33. Jiang C, Huang P (2011) Research of Monitoring VoIP Voice QoS. In: Proceedings of ICICIS 2011, Hong Kong, pp. 499–502

  34. Johannesson NO (1997) The ETSI computation model: a tool for transmission planning of telephone networks. IEEE Commun Mag 35(1):70–79. doi:10.1109/35.568213

    Article  Google Scholar 

  35. Karapantazis S, Pavlidou F-N (2009) VoIP: a comprehensive survey on a promising technology. Comput Netw 53(12):2050–2090. doi:10.1016/j.comnet.2009.03.010

    Article  Google Scholar 

  36. Khan A, Sun L, Jammeh E, Ifeachor E (2010) QoE-driven adaptation scheme for video applications over wireless networks. IET Commun 4(11):1337–1347. doi:10.1049/iet-com.2009.0422

    Article  Google Scholar 

  37. Kim HJ, Choi SG (2014) QoE assessment model for multimedia streaming services using QoS parameters. Multimed Tools Appl 72(3):2163–2175. doi:10.1007/s11042-013-1507-8

    Article  Google Scholar 

  38. Laghari K, Falk T, Hyder M, Haun M, Hoene C, Crespi N (2014) An investigation into the relationship between perceived quality-of-experience and virtual acoustic environments: the case of 3D audio telephony. J Univers Comput Sci 19(12):1718–1735. doi:10.3217/jucs-019-12-1718

    Google Scholar 

  39. Mahdi AE, Picovici D (2009) Advances in voice quality measurement in modern telecommunications. Digit Signal Process 19(1):79–103. doi:10.1016/j.dsp.2007.11.006

    Article  Google Scholar 

  40. Markopoulou A, Iannaccone G, Bhattacharyya S, Chuah C-N, Diot C (2004) Characterization of Failures in an IP Backbone. In: Proceedings of INFOCOM 2004. Hong Kong, pp. 2307–2317

  41. Markopoulou A, Tobagi F, Karam M (2006) Loss and delay measurements of internet backbones. Comput Commun 29(10):1590–1604. doi:10.1016/j.comcom.2005.07.011

    Article  Google Scholar 

  42. MathWorks (2013) Curve Fitting Toolbox. Available at https://www.mathworks.com/products/datasheets/pdf/curve-fitting-toolbox.pdf. Accessed May 2015.

  43. Narbutt M, Davis M (2005) Assessing the quality of VoIP transmission affected by playout buffer scheme. In: Proceedings of MESAQIN 2005, Prague, Paper 15

  44. Ong H-C, Chan S-Y (2011) A Comparison on Neural Network Forecasting. In: Proceedings of Int. Conf. Circuits, System and Simulation, Singapore, pp. 56–60

  45. Praat: doing phonetics by computer. Available at http://www.fon.hum.uva.nl/praat/. Accessed May 2015

  46. Psytechnics (2003) Comparison between subjective listening quality and P.862 PESQ score. Available at http://www.sageinst.com/downloads/960B/wp_sub_v_pesq.pdf. Accessed May 2015

  47. Radonjic V, Ljubisavljevic AK, Stojanovic M (2012) Quality of experience and users elasticity considerations for modelling competition between service providers in NGN. Elektron Elektrotech 18(8):113–116. doi:10.5755/j01.eee.18.8.2640

    Google Scholar 

  48. Ren J, Zhang CM, Huang WC, Mao D (2010) Enhancement to E-model on standard deviation of packet delay. In: Proceedings of ICIS 2010, Chengdu, pp. 256–259

  49. Ren J, Zhang H, Zhu Y, Gao C (2008) Assessment of effects of different language in VOIP. In: Proceedings of ICALIP 2008, Shanghai, pp. 1624–1628

  50. Sittiprapaporn W, Chindaduangratn C, Kotchabhakdi N (2004) Long-term memory traces for familiar spoken words in tonal languages as revealed by the mismatch negativity Songklanakarin. J Sci Technol 26(6):779–786

    Google Scholar 

  51. Sodanil M, Nitsuwat S, Haruechaiyasak C (2010) Thai word recognition using hybrid MLP-HMM. Int J Comput Sci Net Sec 10(3):103–110

    Google Scholar 

  52. Soontayatron S (2010) Socio-Cultural Changes in Thai Beach Resorts: A Case Study of Koh Samui Island, Thailand. Dissertation, Bouenemouth University

  53. Sun L, Mkwawa I-H, Jammeh E, Ifeachor E (2013) Guide to voice and video over IP - for fixed and mobile networks. Springer, London

    Book  Google Scholar 

  54. Takahashi A, Kurashima A, Yoshino H (2006) Objective assessment methodology for estimating Conv.Ersational quality in VoIP. IEEE trans. Audio, Speech, Language Process 14(6):1983–1993

    Article  Google Scholar 

  55. Thanasankit T, Corbitt B (2002) Understanding Thai culture and its impact on requirements engineering process management during information systems development. Asian Acad Manag J 7(1):103–126

    Google Scholar 

  56. Triyason T, Kanthamanon P (2015) E-model modification for multi-languages over IP. Elektron Elektrotech 21(1):82–87. doi:10.5755/j01.eee.21.1.7612

    Google Scholar 

  57. Vatanasakdakul S, Ambra JD (2006) An Exploratory Study of the Socio-Cultural Impact on the Adoption of E-Commerce for Firms in the Tourism Industry of Thailand. In: Proceedings of ECIS 2006, Goteborg, Peper 195

  58. Voznak M (2011) E-model modification for case of cascade codecs arrangement. Int J Math Mod Meth Appl S 5(8):1301–1309

    Google Scholar 

  59. Wutiwiwatchai C, Furui S (2007) Thai speech processing technology: a review. Speech Comm 49(1):8–27. doi:10.1016/j.specom.2006.10.004

    Article  Google Scholar 

  60. Wuttidittachotti P, Daengsi T, Preechayasomboon A, Wutiwiwatchai C, Sukparungsee S (2013) VoIP quality of experience: A study of perceptual voice quality from G.729, G.711 and G.722 with Thai users referring to delay effects. In: Proceedings of ICUFN 2013, Da Nang, pp. 401–406

  61. Zhang H, Xie L, Byun J, Flynn P, Shim Y Packet Loss Burstiness and Enhancement to the E-Model. In: Proceedings of SNPD/SAWN 2005. Towson university, pp 214–219

  62. Zhou X, Muller F, Kooij R E, Van Mieghem, P (2006) Estimation of Voice over IP Quality in the Netherlands. In: Proceedings of IPS-MoMe 2006, Salzburg

Download references

Acknowledgment

Thank you to the Graduate College and the Faculty of Information Technology, KMUTNB for part of funding support. Thank you to SCP Systems Company Ltd. for providing E-model tools for experiments. Thanks all participants and staff/lecturers in KMUTNB, who were involved, particularly Assoc. Prof. Dr. Saowanit Suparungsee the old advisor of the second author and Mr. Gary Sherriff (for his English editing support). Finally, to express the deepest gratitude to the old advisor of the second author who sadly passed away in 2010, the contribution of this paper is especially dedicated to Dr. Gareth Clayton.

Author information

Authors and Affiliations

  1. Department of Data Communication and Networking Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Pongpisit Wuttidittachotti

  2. Department of Enterprise Services, JADS Comm Limited, Bangkok, Thailand

    Therdpong Daengsi

Authors
  1. Pongpisit Wuttidittachotti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Therdpong Daengsi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Therdpong Daengsi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wuttidittachotti, P., Daengsi, T. VoIP-quality of experience modeling: E-model and simplified E-model enhancement using bias factor. Multimed Tools Appl 76, 8329–8354 (2017). https://doi.org/10.1007/s11042-016-3389-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3389-z

Keywords

Search

Navigation