Hearing movement: how taiko can inform automatic recognition of expressive movement qualities

Shannon Cuykendall, Michael J. Junokas, M. Amanzadeh, D. Tcheng, Yawen Wang, T. Schiphorst, Guy E. Garnett, Philippe Pasquier
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引用次数: 3

Abstract

We describe the first stages of exploratory research undertaken to analyze expressive movement qualities of taiko performance, a Japaense artistic practice that combines stylized movement with drumming technique. The eventual goals of this research are to answer 1) Can expressive visual qualities of taiko be heard in the sound and 2) Can expressive sonic qualities of taiko be seen in the movement? We achieved high accuracy across multiple machine-learning algorithms in recognizing key sonic and visual qualities of taiko performance. In contrast to many current methods of studying expressive qualities of movement, we inform our data collection process and annotations with taiko technique. We seek to understand how the fundamentals of taiko create expression. More broadly, we suggest that codified artistic practices, like taiko, can inform automatic recognition and generation of expressive movement qualities that have been challenging to reliably classify, parse, and detect. In future work we propose ways to generalize expressive features of taiko so they can be recognized in other movement contexts.
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听觉运动:太鼓如何通知自动识别表达的运动品质
我们描述了探索性研究的第一阶段,以分析太鼓表演的表现力动作品质,这是一种结合了程式化运动和击鼓技术的日本艺术实践。本研究的最终目标是回答1)在声音中是否可以听到太鼓的表现力视觉品质,2)在动作中是否可以看到太鼓的表现力声音品质。我们通过多种机器学习算法在识别太鼓演奏的关键声音和视觉品质方面实现了高精度。与目前许多研究动作表达性质的方法不同,我们使用太鼓技术来告知我们的数据收集过程和注释。我们试图理解太鼓的基本原理是如何创造表达的。更广泛地说,我们认为编纂的艺术实践,如太鼓,可以为自动识别和生成表达性运动品质提供信息,这些品质一直具有挑战性,难以可靠地分类、解析和检测。在未来的工作中,我们将提出一些方法来概括太鼓的表达特征,以便在其他动作语境中识别它们。
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