“Translating Expression in Taiko Performance” presented by Cuykendall, Junokas, Lee, Amanzadeh, Tcheng, Wang, Schiphorst, Garnett and Pasquier

(Short paper)

Keywords: Taiko, Machine Learning, Dance, Choreography, Expressive Movement Recognition, Sound Recognition, Movement Classification, Musical Gesture, Interactive Installation, Kinesthetic Awareness, Visualization.

We describe our approach to collecting, analyzing and visualizing expressive movement data to support the creation of an interdisciplinary performance and installation work, 3 Movements in Translation. We seek to understand how three perspectives (the performer, the audience, and the machine) can inform one another to create a cross-cultural performance that allows a broad audience to kinesthetically engage and empathize with expressive features of taiko performance. Taiko is a Japanese artistic practice that combines stylized movement with drumming technique. We share initial results in machinelearning analysis of taiko sound and movement. These results are used, in combination with a performer’s perspective, to inform artistic visualizations of important expressive features within taiko. Through this process, we explore how multiple perspectives of taiko can inform the translation process between the performer, the audience and the machine.