Random neural networks (RNN) have been efficiently used as learning tools in many applications of different types. The learning procedure followed so far is the gradient descent one. In this paper we explore the use of the Levenberg—Marquardt (LM) optimization procedure, more powerful when it is applicable, together with one of its major extensions, the LM procedure with adaptive momentum. We show how these methods can be used with RNN and run several experiments to evaluate their performances. The use of these techniques in the case of RNN lead to similar conclusions than when using standard artificial neural network: they clearly improve the learning efficiency.

An important and unsolved problem today is that of automatic quantification of the quality of video flows transmitted over packet networks. In particular, the ability to perform this task in real time (typically for streams sent themselves in real time) is especially interesting. The problem is still unsolved because there are many parameters affecting video quality, and their combined effect is not well identified and understood. Among these parameters, we have the source bit rate, the encoded frame type, the frame rate at the source, the packet loss rate in the network, etc. Only subjective evaluations give good results but, by definition, they are not automatic. We have previously explored the possibility of using artificial neural networks (NNs) to automatically quantify the quality of video flows and we showed that they can give results well correlated with human perception. In this paper, our goal is twofold. First, we report on a significant enhancement of our method by means of a new neural approach, the random NN model, and its learning algorithm, both of which offer better performances for our application. Second, we follow our approach to study and analyze the behavior of video quality for wide range variations of a set of selected parameters. This may help in developing control mechanisms in order to deliver the best possible video quality given the current network situation, and in better understanding of QoS aspects in multimedia engineering.

Traditionally, QoS has been addressed by using network measurements (e.g., loss rates and delays), and little attention has been paid to the quality perceived by end-users of the applications running over the network. Here, we address the issue of integrating speech quality subjective scores and network parameters measurements, for designing control algorithms that would yield the best QoS that could be delivered under a given communications network situation. First, we build a neural network based automaton to measure speech quality in real time, at the style of a group of human subjects when participating in an MOS test. We consider the effects of changes in network parameters (e.g., packetization interval, packet loss rate and their pattern distribution) and encoding on speech signals transmitted over the network. Our database includes transmitted speech signals in different languages. Then, we outline a control mechanism which, based on the application performance within a session (i.e., MOS speech quality scores generated by the neural networks), dynamically adjusts parameters (codec and packetization interval). Finally, we analyze preliminary results to show two main benefits: first, a better use of bandwidth, and second, delivery of the best possible speech quality given the network current situation.