Abstract
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 patterns 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 pro ject between ethnomusicology, cognitive sciences and computer science, aimed at the study of Tunisian Modal Music. Une méthodologie d'extraction automatique de motifs répétés dans des séquences temporelles est présentée, dédiée en particulier à l'analyse de séquences musicales. L'approche initiale consiste en une recherche de motifs fermés dans un espace paramétrique multidimensionnel. Il est montré que ce premier mécanisme doit être articulé avec un système de découverte de motifs périodiques, ce qui implique un parcours strictement chronologique de la séquence. Cette modélisation permet d'éviter un filtrage global des patterns, et donc d'obtenir des résultats présentant une richesse et une pertinence élevée. La modélisation a été intégrée au sein d'un projet collaboratif entre ethnomusicologie, sciences cognitives et informatique, dédié à l'étude de la musique modale tunisienne.
Highlights
This paper introduces a new methodology for repeated pattern extraction in symbolic sequences, and is applied to the analysis of musical scores
Among the different approaches that can be considered for time-series data analysis, one domain of research that has received much attention is the problem of extraction of motives, i.e. the discovery of patterns appearing frequently in time-series data [1, 2, 3, 4]
The approach presented in this paper follows this idea of closed pattern, which is defined here in a multidimensional parametric space
Summary
This paper introduces a new methodology for repeated pattern (or motif) extraction in symbolic sequences, and is applied to the analysis of musical scores. Among the different approaches that can be considered for time-series data analysis, one domain of research that has received much attention is the problem of extraction of motives, i.e. the discovery of patterns appearing frequently in time-series data [1, 2, 3, 4]. The approach presented in this paper follows this idea of closed pattern, which is defined here in a multidimensional parametric space. Another combinatorial redundancy problem, provoked by immediate succession of same patterns, is solved by introducing the concept of cyclic pattern. The model has been applied to the automated motivic analysis of musical scores, and in particular to the study of Arabic improvisations played by Tunisian masters
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