Objective assessment of audio quality

Palomar, D.; Skehill, Ronan; Rice, I.; Picovici, Dorel; McGrath, Sean

doi:10.1049/cp:20080635

Cited by 7 publications

(3 citation statements)

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“…In this study, traffic measurement and analysis processes were combined (details are included in Section 3) to create an integrated QoS evaluation approach. The proposed approach deals with some drawbacks of subjective and objective measurement approaches introduced in [1, 12]. It considers the applications’ QoS requirements, interprets them without the need for complex mathematical models and ensures real‐time operation.…”

Section: Introductionmentioning

confidence: 99%

“…The QoS associated with delivering an application by a network can be evaluated by analysing or measuring its traffic. Analysis techniques examine the characteristics of the traffic [8–11], whereas the measurement techniques determine how well the network treats the traffic [12–14].…”

Section: Introductionmentioning

confidence: 99%

“…QoS measurement can be objective or subjective as well as passive or active. Objective approaches measure QoS by quantitative processes that compare the original and distorted multimedia data [12]. Subjective approaches obtain individuals’ views of the applications’ quality [1].…”

Section: Introductionmentioning

confidence: 99%

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Multimedia traffic quality of service management using statistical and artificial intelligence techniques

Dogman

Saatchi

2014

IET Circuits, Devices & Systems

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Managing quality of service (QoS) is an important network operation, especially in hybrid wired and wireless multimedia networks. In this study, a two-stage approach to intelligently manage QoS for multimedia traffic was developed. Voice over Internet protocol (VoIP) was included in the study as an example of a typical multimedia application. Initially an adaptive statistical sampling technique was employed. It determined the traffic's statistics and used them in a fuzzy inference system to determine the optimum interval between every two consecutive sections of the traffic sampled. In the second stage, a fuzzy c-means (FCM) clustering was used to pre-process QoS parameters (delay, jitter and packet loss ratio) obtained from the devised sampling scheme. A multilayer perceptron (MLP) neural network then used the information from FCM to assess the QoS provided for VoIP. It was shown that the developed adaptive statistical sampling represents the traffic more correctly than the systematic, stratified and random non-adaptive sampling methods. Also, the combination of statistical sampling followed by FCM and MLP accurately indicated the QoS for VoIP.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%