Over the last decade, the internet industry has rapidly grown with regard to infrastructure and bandwidth. Widespread internet networks with large bandwidth connect people-to-people, people-to-machines, and machine-to-machine. Like other multimedia services, large bandwidth enables voice services to be provided over IP networks where network connectivity is not consistent. In this context, research on service quality monitoring is necessary to satisfy customers by providing consistent service quality.
The major contribution of this dissertation is the development of three novel techniques to improve or measure voice quality over IP networks. This dissertation first addresses an adaptive playout buffer scheduling algorithm that enables systems to lossen delay jitter due to the legacy of packet-switched networks. The scheduling algorithm is operated by a desired quality of service, minimizing the end-to-end delay by adjusting playout delay times. Secondly, this dissertation also explores a parameter-based nonintrusive speech quality measure to monitor the quality of VoIP. During the lifetime of sound, the network parameters are estimated and used to predict the quality of speech. As a cognitive model, a machine-learning technique is exploited to map features in the feature space into the perceived speech quality scale space. Finally, this dissertation introduces a signal-based nonintrusive speech quality measure. Features for the proposed measurement are extracted from observations of the characteristics of natural speech sounds and artificial noises. The calculated features are mapped into the perceived speech quality scale. The proposed parameter-based measure achieves a high prediction accuracy while the signal-based measure reaches to a comparable performance to the official International Telecommunication Union (ITU) standard, P.563. The contributions described in this dissertation provides smart methodologies for monitoring or enhancement of VoIP service qualities. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2010-08-1768 |
| Date | 13 December 2010 |
| Creators | Chi, Sanghyun |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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