Abstract EN:

Since the 1980s a new computational model merging concepts from neural networks and queuing theory was developed. The model was introduced under the name of Random Neural Networks (RNNs), inside the field of Neural Networks. In this thesis, a first contribution consists of an adaptation of quasi-Newton optimisation methods for training the RNN model. In the last decade, a new computational paradigm was introduced in the field of Machine Learning, under the name of Reservoir Computing (RC). One of the pioneers and most diffused RC methods is the Echo State Network (ESN) model. Here, we propose a method based on topographic maps to initialise the ESN procedure. Another contribution of the thesis is the introduction of a new RC model called the Echo State Queueing Network (ESQN), where we use ideas coming from RNNs for the design of the reservoir. An ESQN consists of an ESN where the reservoir has a new dynamics inspired by recurrent RNNs. In this thesis, we position the ESQN method in the global Machine Learning area, and provide examples of their use and performances. Finally, we propose a method for real–time estimation of Speech Quality using the learning tools above described. Audio quality in the Internet can be strongly affected by network conditions. As a consequence, many techniques to evaluate it have been developed. In particular, the ITU-T adopted in 2001 a technique called Perceptual Evaluation of Speech Quality (PESQ) to automatically measuring speech quality. PESQ is a well-known and widely used procedure, providing in general an accurate evaluation of perceptual quality by comparing the original and received voice sequences. The thesis provides a procedure for estimating PESQ output working only with measures taken on the network state and using some properties of the communication system, without any original signal. The experimental results obtained prove the capability of our approach to give good estimations of the speech quality in a real–time context.