CONDUCT: An Expressive Conducting Gesture Dataset for Sound Control

Lei Chen 1 Sylvie Gibet 1 Camille Marteau 2
1 EXPRESSION - Expressiveness in Human Centered Data/Media
UBS - Université de Bretagne Sud, IRISA-D6 - MEDIA ET INTERACTIONS
Abstract : Recent research in music-gesture relationship has paid more attention on the sound variations and its corresponding gesture expressive-ness. In this study we are interested by gestures performed by orchestral conductors, with a focus on the expressive gestures made by the non dominant hand. We make the assumption that these gestures convey some meaning shared by most of conductors, and that they implicitly correspond to sound effects which can be encoded in musical scores. Following this hypothesis, we defined a collection of gestures for musical direction. These gestures are designed to correspond to well known functional effect on sounds, and they can be modulated to vary this effect by simply modifying one of their structural component (hand movement or hand shape). This paper presents the design of the gesture and sound sets and the protocol that has led to the database construction. The relevant musical excerpts and the related expressive gestures have been first defined by one expert musician. The gestures were then recorded through motion capture by two non experts who performed them along with recorded music. This database will serve as a basis for training gesture recognition system for live sound control and modulation.
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Submitted on : Monday, July 30, 2018 - 10:15:50 AM
Last modification on : Thursday, February 7, 2019 - 3:04:55 PM
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Lei Chen, Sylvie Gibet, Camille Marteau. CONDUCT: An Expressive Conducting Gesture Dataset for Sound Control. Language Resources and Evaluation Conference (LREC 2018), May 2018, Miyazaki, Japan. ⟨hal-01851389⟩

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