Tagging Choreographic Data for Data Mining and Classification

Abstract : We propose an original approach for mapping the choreographic data into a new representation language adapted to data mining techniques. Our approach relies mainly on the notion of "dance tags" that we took from the NLP community by analogy with Part-of-Speech tagging. The process starts from scores described in Labanotation and produces in a fully automatic manner a high-level, comprehensive representation of the choreographic sequence. Our experiments show that we succeed in retrieving manually translated scores with an accuracy of 85% to 94%. Using this new representation of the choreographic data, one can then perform several useful tasks in an efficient manner. Among these are: music recommendation, automated detection of dance style or genre, and ultimately any task that requires a deeper understanding of the meaning of choreographic information than traditional processing can provide. In this paper, we demonstrate the usefulness of our approach with a simple example for discriminating between classical ballet, modern ballet, and folkloric dances.
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https://hal.univ-lyon2.fr/hal-01953434
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Catalina Anca Ioan, Julien Velcin, Stefan Trausan-Matu. Tagging Choreographic Data for Data Mining and Classification. 24th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Jul 2013, Athens, Greece. ⟨hal-01953434⟩

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