Learning in the recurrent random neural network
Neural Computation
Traffic and video quality with adaptive neural compression
Multimedia Systems - Special issue on multimedia networking
Random neural networks with multiple classes of signals
Neural Computation
G-networks: new queueing models with additional control capabilities
Proceedings of the 1995 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Voice over IP performance monitoring
ACM SIGCOMM Computer Communication Review
Design and performance of cognitive packet networks
Performance Evaluation
Sensor Fusion for Mine Detection with the RNN
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Stability of the Random Neural Network Model
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Networks with Cognitive Packets
MASCOTS '00 Proceedings of the 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
On the Suitability of the E-Model to VoIP Networks
ISCC '02 Proceedings of the Seventh International Symposium on Computers and Communications (ISCC'02)
Cognitive Packet Networks: QoS and Performance
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Enhanced modified bark spectral distortion (embsd): an objective speech quality measure based on audible distortion and cognition model
Internet telephony: services, technical challenges, and products
IEEE Communications Magazine
Video quality and traffic QoS in learning-based subsampled and receiver-interpolated video sequences
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
Demand management for telecommunications services
Computer Networks: The International Journal of Computer and Telecommunications Networking
Quality assessment of interactive voice applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
Video quality assurance in multi-source streaming techniques
Proceedings of the 4th international IFIP/ACM Latin American conference on Networking
Multiclass G-Networks of Processor Sharing Queues with Resets
ASMTA '08 Proceedings of the 15th international conference on Analytical and Stochastic Modeling Techniques and Applications
QoE monitoring platform for video delivery networks
IPOM'07 Proceedings of the 7th IEEE international conference on IP operations and management
A study on QoS of VoIP networks: a random neural network (RNN) approach
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Networks of symmetric multi-class queues with signals changing classes
ASMTA'10 Proceedings of the 17th international conference on Analytical and stochastic modeling techniques and applications
Learning in the feed-forward random neural network: A critical review
Performance Evaluation
An initiative for a classified bibliography on G-networks
Performance Evaluation
WWIC'11 Proceedings of the 9th IFIP TC 6 international conference on Wired/wireless internet communications
Evaluating users’ satisfaction in packet networks using random neural networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Bibliography on G-networks, negative customers and applications
Mathematical and Computer Modelling: An International Journal
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This paper addresses the problem of quantitatively evaluating the quality of a speech stream transported over the Internet as perceived by the end-user. We propose an approach being able to perform this task automatically and, if necessary, in real time. Our method is based on using G-networks (open networks of queues with positive and negative customers) as Neural Networks (in this case, they are called Random Neural Networks) to learn, in some sense, how humans react vis-a-vis a speech signal that has been distorted by encoding and transmission impairments. This can be used for control purposes, for pricing applications, etc.Our method allows us to study the impact of several source and network parameters on the quality, which appears to be new (previous work analyzes the effect of one or two selected parameters only). In this paper, we use our technique to study the impact on performance of several basic source and network parameters on a non-interactive speech flow, namely loss rate, loss distribution, codec, forward error correction, and packetization interval, all at the same time. This is important because speech/audio quality is affected by several parameters whose combined effect is neither well identified nor understood.