تعداد نشریات | 26 |
تعداد شمارهها | 550 |
تعداد مقالات | 5,698 |
تعداد مشاهده مقاله | 7,963,640 |
تعداد دریافت فایل اصل مقاله | 5,347,417 |
Developing Fuzzy Models for Estimating the Quality of VoIP | ||
Iranian Journal of Fuzzy Systems | ||
مقاله 4، دوره 11، شماره 1، بهار 2014، صفحه 49-73 | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22111/ijfs.2014.1395 | ||
نویسندگان | ||
F. Rahdari ![]() | ||
1Computer and Information Technology Department, Institute of Sci- ence and High Technology and Environmental Sciences, Graduate University of Ad- vanced Technology, Kerman, Iran | ||
2Computer Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran | ||
3Computer Engineering Department, Iran University of Science and Tech- nology, Tehran, Iran | ||
چکیده | ||
This paper presents a novel method for modeling the one-way quality prediction of VoIP, non-intrusively. Intrusive measures of voice quality suffer from common deficiency that is the need of reference signal for evaluating the quality of voice. Owing to this lack, a great deal of effort has been recently devoted for modeling voice quality prediction non-intrusively according to quality degradation parameters, while among the past proposed methods, intelligent techniques have been remarkably successful due to their abilities for modeling the non-linear processes. The present study introduces a procedure for developing fuzzy models, employing Genetic Algorithm (GA) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed method is able to generate optimized fuzzy models in terms of accuracy and complexity. The efficiency of this procedure is compared with and contrasted against 13 regression methods implemented in KEEL as one machine learning tool. Moreover, several experimental results are performed over voice data from 10 different languages. In order to complete the experiment, a comprehensive statistical comparison is also drawn between our proposed method and other previous ones. The results apparently show the efficiency and applicability of this novel method in terms of generating accurate and simple fuzzy models for estimating the VoIP quality. | ||
کلیدواژهها | ||
VoIP؛ Voice quality؛ Non-intrusive prediction؛ PESQ؛ Neuro-fuzzy؛ GA؛ KEEL | ||
مراجع | ||
bibitem{Alcala-Fdez:KEEL} J. Alcala-Fdez, L. Sanchez, S. Garcia, M. del Jesus, S. Ventura, J. Garrell, J. Otero, C. Romero, J. Bacardit, V. Rivas, J. Fernandez and F. Herrera, {it KEEL: a software tool to assess evolutionary algorithms for data mining problems}, Journal of Soft Computing - A Fusion of Foundations, Methodologies and Applications, {bf 13}textbf{(3)} (2009), 307-318. bibitem{Andersen:iLBC}
S. Andersen and A. Duric, {it Internet low bit rate codec (iLBC),IETF draft}, 2002.
bibitem{Beerends:PESQ} J. G. Beerends, A. P. Hekstra, A. W. Rix and M. P. Hollier, {it Perceptual evaluation of speech quality (PESQ): the new ITU standard for end-to-end speech quality assessment part II - psychoacoustic model}, Journal of Audio Eng. Soc., {bf 50}textbf{(10)} (2002), 765-778. bibitem{Bolot:PLandD} J. Bolot, {it Characterizing end-to-end packet delay and loss in the Internet}, Journal of High-Speed Networks, {bf 2}textbf{(3)} (1993), 305-323. bibitem{Borella:MIofPL} M. S. Borella, {it Measurement and interpretation of Internet packet loss}, Journal of Communication and Networking, {bf 2} (2000), 93-102. bibitem{Clark:MofBPL} A. D. Clark, {it Modeling the effects of burst packet loss and recency on subjective voice quality}, Proc. of IPTEL2001, New York, USA, (2001), 123-127. bibitem{Cole:VoIP-PM} R. G. Cole and J. Rosenbluth, {it Voice over IP performance monitoring}, Journal of ACM Computing Communication Review, {bf 31}textbf{(2)} (2001), 9-24. bibitem{Cox:3NSC} R. Cox, {it Three new speech coders from the ITU cover a range of applications}, Journal of IEEE Communications Magazine, {bf 35}textbf{(9)} (1997), 40-47. bibitem{Demsar:SCCoverMDS} J. Demsar, {it Statistical comparisons of classifiers over multiple data sets}, Journal of Machine Learning Research, {bf 7} (2006), 1-30. bibitem{Eftekhari:CFRforNSM} M. Eftekhari and S. D. Katebi, {it Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter}, Journal of Applied Mathematical Modeling, {bf 32} (2008), 2634-2651. bibitem{Eftekhari:TFM-DE} M. Eftekhari, S. D. Katebi, M. Karimi and A. H. Jahanmiri, {it Eliciting transparent fuzzy model using differential evolution}, Journal of Applied Soft Computing , {bf 8} (2008), 466-476. bibitem{Garcia:E-SCCoverMDS} S. Garcia and F. Herrera, {it An Extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons}, Journal of Machine Learning Research, {bf 9} (2008), 2677-2694. bibitem{Herrera:GFS} F. Herrera, {it Genetic fuzzy systems: taxonomy, current research trends and prospects}, Journal of Evolutionary Intelligence, {bf 1}textbf{(1)} (2008), 27-46. bibitem{ITU:P.50} International Telecommunication Union, {it Objective measuring apparatus, Appendix 1: test signals}, ITU-T Recommendation P.50, 1998. bibitem{ITU:MOS} International Telecommunication Union, {it Mean opinion score (MOS) terminology}, ITU-T Recommendation P.800.1, 2003. bibitem{ITU:PESQ} International Telecommunication Union, {it Perceptual evaluation of speech quality (PESQ), an objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs}, ITU-T Recommendation P.862, 2001. bibitem{ITU:H323} International Telecommunication Union, {it Packet based multimedia communications systems}, ITU-T Recommendation H.323, 1998. bibitem{ITU:E-MODEL} International Telecommunication Union, {it The E-model, a computational model for use in transmission planning}, ITU-T Recommendation G.107, 2000. bibitem{ITU:P800} International Telecommunication Union, {it Methods for subjective determination of transmission quality}, ITU-T Recommendation P.800, 1996. bibitem{Jang:ANFIS} J. S. R. Jang, {it ANFIS: adaptive network-based fuzzy inference systems}, Journal of IEEE Transactions on System, Man and Cybernetics, {bf 23} (1993), 665-685. bibitem{Jang:NFandSC} J. S. R. Jang, C. T. Sun and E. Mizutani, {it Neuro-fuzzy and soft computing}, Prentice Hall, Engleeood Cliffs, 1977. bibitem{Jiang:PLandD} W. Jiang and H. Schulzrinne, {it Modeling of packet loss and delay and their effect on real-time multimedia service quality}, Proc. of Int.Workshop Network and Operating Systems Support for Digital Audio and Video NOSSDAV, Chapel Hill, NC, 2000. bibitem{Kurose:CN-TD} J. F. Kurose and K. W. Ross, {it Computer networking: a top-down approach featuring the Internet}, Pearson Addison-Wesley, 2000. bibitem{Markopoulou:VoIPQ-IB} A. P. Markopoulou, F. A. Tobagi and M. Karam, {it Assessment of VoIP quality over Internet backbones}, Proc. of IEEE Infocom, (2002), 150-159. bibitem{Nelles:NSI} O. Nelles, {it Nonlinear system identification: from classical approaches to neural networks and fuzzy models}, Springer, Berlin Heidelberg, 2000. bibitem{Perkins:PLRforSA} C. Perkins, O. Hodson and V. Hardman, {it A survey of packet loss recovery techniques for streaming audio}, Journal of IEEE Network, {bf 12} (1998), 40-48. bibitem{Rahdari:FM-GANF} F. Rahdari and M. Eftekhari, {it Developing fuzzy models for estimating quality of VoIP using a hybrid of GA and neuro-fuzzy}, Proc. of 2nd Int. Conf. on Contemporary Issues in Computer and Information Sciences (CICIS), Zanjan, Iran, (2011), 197-201 bibitem{Rahdari:VQ-NF} F. Rahdari and M. Eftekhari, {it Modeling the perceived voice quality for VoIP system based on Neuro-Fuzzy}, Proc. of Int. Conferences on Computer and knowledge Engineering (ICCKE), Mashhad, Iran, (2011), 81-86 bibitem{Rahdari:BCforQVoIP} F. Rahdari and M.Eftekhari, {it Using bayesian classifiers for estimating quality of VoIP}, Proc. of 16th CSI Int. symposium on Artificial Intelligence and Signal Processing (AISP), Shiraz, Iran, (2012), 348-353. bibitem{Raja:NIQE-GP} A. Raja, R. Azad, C. Flanagan and C. Ryan, {it Non-intrusive quality evaluation of VoIP using genetic programming}, Proc. of 1st Int. Conference on Bio- inspired Models of Network, Information and Computing Systems, (2006), 1-8 bibitem{Rosenberg:SIP} J. Rosenberg, H. Schulzrinne, G. Camarillo, A. Johnston, J. Peterson, R. Sparks, M. Handley and E. Schooler, {it SIP: Session Initiation Protocol}, RFC 3261, 2002. bibitem{Sanchez:RSMforIFM} L. Sanchez, {it A random sets-based method for identifying fuzzy models}, Journal of Fuzzy Sets and Systems, {bf 98}textbf{(3)} (1998), 343-354. bibitem{Sanneek:FMforPL} H. Sanneek, G. Carle and R. Koodli, {it A framework model for packet loss metrics based on run length}, Proc. of SPIE/ACM SIGMM Multimedia Computing and Networking Conf., 2000. bibitem{Schulzrinne:RTP} H. Schulzrinne, S. Casner, R. Frederick and V. Jacobson, {it RTP: a transport protocol for real-time applications}, RFC 1889, 1996. bibitem{Sun:SQPforIPNet} L. Sun and E. Ifeachor, {it Perceived speech quality prediction for voice over IP-based networks}, Proc. of IEEE Int. Conf. Communications ICC02, New York, (2002), 2573-2577. bibitem{Sun:VQPinVoIP} L. Sun and E. Ifeachor, {it Voice quality prediction models and their application in VoIP network}, Journal of IEEE Trans. On Multimedia, {bf 8}textbf{(4)} (2006), 809-820. bibitem{Sun:SandOunderBL} L. Sun and E. Ifeachor, {it Subjective and objective speech quality evaluation under bursty losses}, Proc. of on-line Workshop Measurement of Speech and Audio Quality in Networks (MESAQIN), Prague, Czech, (2002), 25-29 bibitem{Wang:GFRbyLE} L. X. Wang and J. M. Mendel, {it Generating fuzzy rules by learning from examples}, Journal of IEEE Transactions on Systems, Man and Cybernetics, {bf 22}textbf{(6)} (1992), 1414-1427. bibitem{Wang:MTforPCC} I. Wang and I. H. Witten, {it Induction of model trees for predicting continuous classes}, Proc. of 9th European Conf. on Machine Learning, Czech Republic, (1997), 128-137. | ||
آمار تعداد مشاهده مقاله: 2,667 تعداد دریافت فایل اصل مقاله: 10 |