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A new methodology for automated extraction of repeated patterns in time series data is presented, aimed in particular at the analysis of musical sequences. The basic principles consists in a search for closed pattern paradigm in a multi-dimensional parametric space. It is shown that this basic mechanism needs to be articulated with a periodic pattern discovery system, implying therefore a strict chronological scanning of the time series data. Thanks to this modelling global pattern filtering may be avoided and rich and highly pertinent results can be obtained. The modelling has been integrated in a collaborative project between ethnomusicology, cognitive sciences and computer science, aimed at the study of Tunisian Modal Music.