http://popups.lib.uliege.be/1373-5411/index.php?id=482 Publications of Auteurs Giovanna Morgavi fr 0 http://popups.lib.uliege.be/1373-5411/index.php?id=1777 The conversation is the most common interaction process in the daily life : the goal of this paper is the extraction of information on the evolution of a communicative process through simple quantitative measurements. The whole interview process has been considered as a complex system evolving in the time. Our approach founds on analogies between conversation processes and chaotic systems. The proposed procedure allowed the extraction of information on the conversation evolution : phase portraits with anomalous paths indicate situations where the communication has been troubled from external references. Some parameters showing very good indication on the process evolution are proposed. Tue, 16 Jul 2024 15:34:52 +0200 Tue, 16 Jul 2024 15:35:04 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=1777 http://popups.lib.uliege.be/1373-5411/index.php?id=477 In this paper a procedure to solve the problem of recognition and classification of sampled musical rythms is presented. The lack of precise rules for doing this analysis makes difficult and often ambiguous the automatic execution of a cognitive process naturally performed by human brain. This procedure can be extended to the classification of any signals showing similar characteristic (i.e. EEG or ECG). Due to the complexity of the time dependence, standard procedures used for chaos characterisation (i.e. correlation dimension, Lyapunov exponents, etc) can fail. Moreover a direct usage of artificial neural network can introduce too many optimization variables. The proposed procedure can be organized in two phases : the extraction of some new type of invariant from the sampled time series and the usage of this extracted features as input for a classifying standard neural network. This system was able to distinguish between binary and ternary signals with a precision of 99 %. The single rhythm was classified within an error of 5 %. This system seems to be able to deal with the behaviour that characterises a musical rhythmic sequence, and to classify patterns independently of the musical instrument and tempo. Thu, 27 Jun 2024 11:50:36 +0200 Tue, 08 Oct 2024 14:02:59 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=477