VoIP quality measurement: subjective VoIP quality estimation model for G.711 and G.729 based on native Thai users

Daengsi, Therdpong; Khitmoh, Nalakkhana; Wuttidittachotti, Pongpisit

doi:10.1007/s00530-015-0468-3

Cited by 9 publications

(12 citation statements)

References 32 publications

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“…This value was kept at 2 as other values either introduced insufficient or large overlap between the clusters. The degrees of membership of n th example case, ( nk  ) belonging to cluster k was indicated by (6) The clustering process was terminated when the objective function improvement between two consecutive iterations was less than the minimum amount (in this study this was 10 -5 ). The number of clusters C was set to three and were chosen based on the Xie-Beni index cluster validity method [17].…”

Section: A Analysis Of Qos Using Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

“…A limitation of this approach was not considering QoS parameters such as delay, jitter and packet loss ratio. QoS monitoring techniques for voice over internet protocol (VoIP) that evaluated the connection characteristics based on active measurement and used Mean Opinion Score (MOS) model (the user experience) were reported [6] [7]. Techniques were reported to evaluate overall QoS for multimedia applications using artificial intelligence that included fuzzy logic and neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Quality of service evaluation and assessment methods in wireless networks

Salama

Saatchi

Burke

2017

2017 4th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

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Wireless networks are capable of facilitating a reliable multimedia communication. The ease they can be deployed is ideal for disaster management. The Quality of Service (QoS) for these networks is critical to their effectiveness. Evaluation of QoS in wireless networks provides information that supports their management. QoS evaluation can be performed in multiple ways and indicates how well applications are delivered. In this work, fuzzy c-means clustering (FCM) and Kohonen unsupervised neural networks were compared for their abilities to differentiate between Good, Average and Poor QoS for voice over IP (VoIP) traffic. Fuzzy inference system (FIS), linear regression and multilayer perceptron (MLP) were evaluated to quantify QoS for VoIP. FCM and Kohonen successfully classified VoIP traffic into three types representing Low, Medium, and High QoS. FIS, regression model and MLP combined the QoS parameters (i.e. delay, jitter, and percentage packet loss ratio) with information from the generated clusters and indicated the overall QoS.

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Section: A Analysis Of Qos Using Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality of service evaluation and assessment methods in wireless networks

Salama

Saatchi

Burke

2017

2017 4th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

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“…It is different from western languages, especially, there are 5 tones, consisting of low-tone, mid-tone, high-tone, rising tone and falling tone [12]. It has been mentioned in [13][14] and proven that native Thai users have better perception to Thai speech than non-Thai native speakers (e.g. Chinese and British) because the left frontal operculum of a Thai brain is activated when listening to Thai speech sounds, which is their mother language, whereas the non-Thai native brain is not activated during the same scenario tests.…”

Section: B Motivation Of Workmentioning

confidence: 97%

“…VoIP is sensitive easily impacted by impairment factors in the IP network, thus, it requires quality of service guarantees [3]. In IP networks, two major impairment factors are delay and loss that quite impact VoIP quality [13,23]. In general, loss occurs when packets are transmitted but a few are not received at the receiver due to events occurring in the IP network.…”

Section: B Impairment Factorsmentioning

confidence: 99%

“…1. Examples of random packet (the lower) and bursty packet loss (the upper), adopted from [24] Previously, the subjective MOS prediction models based on native Thai users referring to delay effects and/or loss effects were proposed in [13,39,41]. Its concept is to gather scores from voting by a group of participants or subjects after VoIP quality testing under loss and/re delay scenarios.…”

Section: Subjective Quality Measurement and Subjective Mos Prediction...mentioning

confidence: 99%

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QoE Modeling for Voice over IP: Simplified E-model Enhancement Utilizing the Subjective MOS Prediction Model: A Case of G.729 and Thai Users

Daengsi

Wuttidittachotti

2019

J Netw Syst Manage

Self Cite

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This research proposes an enhanced measurement method for VoIP quality assessment which provides an improvement to accuracy and reliability. To improve the objective measurement tool called the simplified E-model for the selected codec, G.729, it has been enhanced by utilizing a subjective MOS prediction model based on native Thai users, who use the Thai-tonal language. Then, the different results from the simplified E-model and subjective MOS prediction model were used to create the Bias function, before adding to the simplified E-model. Finally, it has been found that the outputs from the enhanced simplified E-model for the G.729 codec shows better accuracy when compared to the original simplified E-model, specially, after the enhanced model has been evaluated with 4 test sets. The major contribution of this enhancement is that errors are reduced by 58.87 % when compared to the generic simplified E-model. That means the enhanced simplified E-model as proposed in this study can provide improvement beyond the original simplified one significantly.

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Quality of experience for unified communications: A survey

et al. 2019

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SummaryThis paper presents a quality of experience (QoE) conceptual model to the context of unified communications (UC) through summary, classification, and discussion of multiple influence factors (IFs) and dimensions affecting it. A deep and comprehensive understanding of the IFs and their impact on QoE for a given service is an essential precondition for successful QoE management with the overall goal of prominently optimizing end‐user QoE, while making efficient use of network resources and maintaining a satisfied user base. The proposed conceptual model was used to conduct a qualitative meta‐analytical review of selected papers. The results of the qualitative review include various IFs, QoE dimensions, and key findings in the form of research recommendations for QoE in the context of UC.

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VoIP quality measurement: subjective VoIP quality estimation model for G.711 and G.729 based on native Thai users

Cited by 9 publications

References 32 publications

Quality of service evaluation and assessment methods in wireless networks

Quality of service evaluation and assessment methods in wireless networks

QoE Modeling for Voice over IP: Simplified E-model Enhancement Utilizing the Subjective MOS Prediction Model: A Case of G.729 and Thai Users

Quality of experience for unified communications: A survey

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