Stability of the random neural network model
Neural Computation
Audio Quality Assessment in Packet Networks: an "Inter-Subjective" Neural Network Model
ICOIN '01 Proceedings of the The 15th International Conference on Information Networking
Assessing the quality of voice communications over internet backbones
IEEE/ACM Transactions on Networking (TON)
A Study for Providing Better Quality of Service to VoIP Users
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 01
Controlling Multimedia QoS in the Future Home Network Using the PSQA Metric
The Computer Journal
Quality assessment of interactive voice applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
The Computer Journal
A study of real-time packet video quality using random neural networks
IEEE Transactions on Circuits and Systems for Video Technology
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Voice over Internet Protocol (VoIP) is predicted to be the replacement of the traditional PSTN telephone system. Quality of Service (QoS) of VoIP systems are more difficult to measure and implement compared to PSTN systems. The nature of QoS in VoIP networks is very variable and hence it is important to be able to measure the QoS offered by the system in real time with a low computational cost. So it is very important to measure the quality of service offered by VoIP networks. In this paper we discuss a new novel model to calculate the perceived voice quality using Random Neural Network (RNN). The RNN is an open Markovian queuing model, motivated by spiking behaviour of biological neurons that has been used for several applications. We used the feedforward architecture with different numbers of hidden neurons to test the stability of our model. We study the RNN model with 4, 5, and 6 hidden layers of neurons. Our model shows a high degree of accuracy