Objective non-intrusive conversational voip quality prediction using data mining methods

Journal article

Authors/Editors

Strategic Research Themes

No matching items found.

Publication Details

Author list: Valaisathien S., Vanijja V.

Publisher: Springer Science Business Media

Publication year: 2015

Journal: Lecture Notes in Electrical Engineering (1876-1100)

Volume number: 339

Start page: 135

End page: 142

Number of pages: 8

ISSN: 1876-1100

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923117763&doi=10.1007%2f978-3-662-46578-3_16&partnerID=40&md5=ed521c747863597b98f54fae7d22c757

Languages: English-Great Britain (EN-GB)

View on publisher site

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. ฉ Springer-Verlag Berlin Heidelberg 2015.

Keywords

E-model