Abstract
Nowadays, there is a growth in the number of applications running on the Internet involving real-time transmission of speech and audio streams. Among these applications, Voice over Internet Protocol (VoIP) has become a widespread application based on the Internet Protocol (IP). However, its quality-of-service (QoS) is not robust to network impairments and codecs. It is hard to determine conversational voice quality within real-time network by using ITU-T standards, PESQ and E-model. In this research, three data mining methods: Regression-based, Decision tree and Neural network were used to create the prediction models. The datasets were generated from the combination of PESQ and E-model. The statistical error analysis was conducted to compare accuracy of each model. The results show that the Neural network model proves to be the most suitable prediction model for VoIP quality of service.
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References
ITU-T Recommendation P.862: Perceptual evaluation of speech quality (PESQ), an objective method for end-to-end speech quality assessment of narrowband telephone networks and speech codecs, (2001)
ITU-T Recommendation G.107 The E-model a computational model for use in transmission planning, (2014)
Sun, L., Ifeachor, E.C.: Voice quality prediction models and their application in VoIP networks. IEEE Transactions on Multimedia. 8, 809–820 (2006).
Sun, L., Ifeachor, E.C.: Perceived speech quality prediction for voice over IP-based networks. IEEE International Conference on Communications, 2002. ICC 2002. pp. 2573–2577 vol.4 (2002)
Mohamed, S., Rubino, G., Varela, M.: Performance evaluation of real-time speech through a packet network: a random neural networks-based approach. Performance Evaluation. 57, 141–161 (2004).
ITU-T Recommendation P.800. Methods for subjective determination of transmission quality, (1996)
ITU-T Recommendation G.113: Transmission impairments due to speech processing, (2007)
Cole, R.G., Rosenbluth, J.H.: Voice over IP Performance Monitoring. SIGCOMM Comput. Commun. Rev. 31, 9–24 (2001).
ITU T Recommendation P.50: Artificial voices, (1999)
The Open Speech Repository, http://www.voiptroubleshooter.com/open_speech
ITU-T Recommendation G.711. Pulse code modulation (PCM) of voice frequencies, (1988)
ITU-T Recommendation G.729: Coding of speech at 8 kbit/s using conjugate-structure algebraic-code-excited linear prediction (CS-ACELP), (2012)
Speex: a free codec for free speech, http://www.speex.org/
Goudarzi, M., Sun, L., Ifeachor, E.: Modelling Speech Quality for NB and WB SILK Codec for VoIP Applications. 2011 5th International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST). pp. 42–47 (2011).
Radhakrishnan, K., Larijani, H.: A Study on QoS of VoIP Networks: A Random Neural Network (RNN) Approach. Proceedings of the 2010 Spring Simulation Multiconference. pp. 114:1–114:6. Society for Computer Simulation International, San Diego, CA, USA (2010).
ITU-T Recommendation P800.1: Mean Opinion Score (MOS) terminology (2006)
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Valaisathien, S., Vanijja, V. (2015). Objective Non-intrusive Conversational VoIP Quality Prediction using Data mining methods. In: Kim, K. (eds) Information Science and Applications. Lecture Notes in Electrical Engineering, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46578-3_16
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DOI: https://doi.org/10.1007/978-3-662-46578-3_16
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