These are the main topics addressed within this main research field:
The employment of Neural Networks (NNs) has been extensively addressed for digital signal processing (DSP) applications (like nonlinear system identification, time series prediction, pattern matching and recognition just to name a few) and several architectures and learning algorithms have been proposed. The literature indeed offers a big choice of neural architectures and learning algorithms.
Regarding the former, the primer classification we can make is between recurrent and feedforward neural networks (RNNs and FFNNs) on the basis of the presence or no respectively of loops, typically due to the occurrence of feedback connections. Recurrent structures allows representing context information and therefore a more general class of tasks w.r.t. the feedforward counterpart, even though the training usually presents more difficulties to face.
Concerning the learning strategies, we can distinguish among supervised and unsupervised techniques, depending on the availability of target signals during the parameter adaptation phase. An important class of algorithms employs the usage of gradient-based information related to a certain cost function which normally represents how much distant we are from optimal performing NNs. Such algorithms are easy to implement but also suffers of relevant drawbacks as local minima entrapment and vanishing gradient effect (in the case adaptation of recurrent networks). In this scenario the A3LAB research work finds a location... Read more
Involved People: F. Piazza, S. Fiori, S. Squartini
Blind Signal Processing
Within the wide Digital Signal Processing (DSP) world, a relevant role is played by Adaptive DSP, devoted to those solutions where a structure changing its characteristics in response to specific stimuli and according to certain rules occur. In this field a relevant distinction has to be made, that is between supervised and unsupervised methods employed to carry out the adaptation of the structure chosen for solving the task under study. Usually, the latter seems to be more realistic but more difficult, and sometimes impractical if we do not receive any help from the learning environment. Along this direction, “Blind Signal Processing (BSP) is now one of the hottest and emerging areas in Signal Processing with solid theoretical foundations and many potential applications”, in several areas like biomedical engineering, speech enhancement, communication systems, data mining, and so on. Furthermore, BSP “techniques principally do not use any training data and do not assume any a-priori knowledge about parameters of convolutive, filtering and mixing systems”. Many researchers are involved in this field and lots of scientific contributions have been proposed in the last 10-15 years. Nevertheless the research activity is very alive and new topics and algorithms are continuously investigated resulting in more and more florid literature... Read more
Involved People: S. Fiori, F. Piazza, S. Squartini
A short view on accomplished/on-going Projects related to the artificial intelligence research field:
- Smart Jaw Chunks
- Smart Professional Oven