Title
A_PSQA: PESQ-like non-intrusive tool for QoE prediction in VoIP services
Abstract
Perceived speech quality, or Quality of Experience (QoE), is the key criteria for evaluating VoIP service. Most of the existing solutions are intrusive, in a sense where they require both the original and the transmitted audio sequences. These solutions give good estimation of the QoE, but they cannot be used in real-time. In fact, Service Provider and Network Provider are highly interested on automatic QoE estimation tool (without intrusion) in order to monitor and control the perceived quality of their VoIP service. In this paper, we present a new perceived speech quality estimation tool, named ALICANTE1 Pseudo Subjective Quality Assessment (A_PSQA) for two widely VoIP codecs, iLBC and Speex. A_PSQA is a non-intrusive method, which relies on Random Neural Network (RNN) approach to learn the nonlinear relation between network parameters and the perceived user QoE. Furthermore, to avoid costly and time-consuming subjective tests, we used a well-known intrusive method ITU-T's Perceptual Evaluation Quality (PESQ) to estimate the MOS. Obtained results show that A_PSQA is able to estimate the MOS like PESQ, while being non-intrusive. Besides, A_PSQA's results are compared with two non-intrusive (IQX and E-Model) methods, where A_PSQA shows the highest correlation with PESQ estimation than all others methods.
Year
DOI
Venue
2012
10.1109/ICC.2012.6364004
Communications
Keywords
Field
DocType
Internet telephony,neural nets,quality of service,speech codecs,A_PSQA,Alicante pseudo subjective quality assessment,PESQ-like nonintrusive tool,QoE prediction,RNN approach,Speex VoIP codecs,VoIP services,audio sequences,automatic QoE estimation tool,iLBC VoIP codecs,intrusive method ITU-T perceptual evaluation quality,network provider,nonintrusive E-model methods,nonintrusive IQX method,perceived speech quality estimation tool,quality of experience,random neural network approach,service provider,E-model,MOS,PESQ,Perceived Speech Quality,QoE,Speex,iLBC
Random neural network,Speex,Computer science,PSQM,Quality of service,Computer network,Service provider,Quality of experience,PESQ,Voice over IP
Conference
ISSN
ISBN
Citations 
1550-3607 E-ISBN : 978-1-4577-2051-2
978-1-4577-2051-2
6
PageRank 
References 
Authors
0.62
11
4
Name
Order
Citations
PageRank
Wael Cherif1181.37
Adlen Ksentini2108593.20
Daniel Negru38619.91
M. Sidibé4343.10