Stability of the random neural network model
Neural Computation
Traffic and video quality with adaptive neural compression
Multimedia Systems - Special issue on multimedia networking
Audio Quality Assessment in Packet Networks: an "Inter-Subjective" Neural Network Model
ICOIN '01 Proceedings of the The 15th International Conference on Information Networking
Analog Hardware Implementation of the Random Neural Network Model
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Assessing the quality of voice communications over internet backbones
IEEE/ACM Transactions on Networking (TON)
Voice-Quality Monitoring and Control for VoIP
IEEE Internet Computing
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
Voice Quality in VoIP Networks Based on Random Neural Networks
ICN '10 Proceedings of the 2010 Ninth International Conference on Networks
A study on QoS of VoIP networks: a random neural network (RNN) approach
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Pure delay effects on speech quality in telecommunications
IEEE Journal on Selected Areas in Communications
A study of real-time packet video quality using random neural networks
IEEE Transactions on Circuits and Systems for Video Technology
Function approximation with spiked random networks
IEEE Transactions on Neural Networks
Learning in the multiple class random neural network
IEEE Transactions on Neural Networks
Review: VoIP: State of art for global connectivity-A critical review
Journal of Network and Computer Applications
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Voice over Internet Protocol (VoIP) is one of the fastest growing technologies in the world. In VoIP speech signals are transmitted over the same network used for data communications. The internet is not a robust network and is subjected to delay, jitter, and packet loss. It is very important to measure and monitor the quality of service (QoS) the users experience in VoIP networks; this is not an easy task and usually requires subjective tests. In this paper we have analyzed three non-intrusive models to measure and monitor voice quality using Random Neural Networks (RNN). A RNN is an open queuing network with positive and negative signals. We have assessed the voice quality based on various parameters i.e. delay, jitter, packet loss, and codec. In our approach we have used the Mean Opinion Score (MOS) calculated using a Perceptual Evaluation of Speech Quality (PESQ) algorithm to generate data for training the RNN model. We have studied two feed-forward models and a recurrent architecture. We have found that the simple feed-forward architecture has produced the most accurate results compared to the other two architectures.