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Meeting Abstract

Motif-gesture clustering in Karnatak vocal performance: A multimodal computational music analysis

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Pearson,  Lara       
Department of Music, Max Planck Institute for Empirical Aesthetics, Max Planck Society;

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mus-23-pea-03-motif.pdf
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Citation

Pearson, L., Nuttall, T., & Pouw, W. (2023). Motif-gesture clustering in Karnatak vocal performance: A multimodal computational music analysis. In M. Tsuzaki, M. Sadakata, S. Ikegami, T. Matsui, M. Okano, & H. Shoda (Eds.), The e-proceedings of the 17th International Conference on Music Perception and Cognition and the 7th Conference of the Asia-Pacific Society for the Cognitive Sciences of Music (pp. 117-117). Tokyo: College of Art, Nihon University.


Cite as: https://hdl.handle.net/21.11116/0000-000E-AFCD-7
Abstract
As music is increasingly understood as multimodal rather than purely sonic, there has been a growth in research
on co-singing bodily movement. In Indian art music contexts, connections have been noted between bodily
gestures and musical motifs (Rahaim 2012), but systematic analyses remain scarce. Here we draw on co-speech
gesture studies where catchments have been theorized as regions of recurring gestures that index underlying
discourse themes (McNeill 2000; Pouw and Dixon 2020). We analyze a dataset of 5.42 hours of Karnatak (South
Indian) vocal performances (audio, video, motion-capture), investigating how performer gestures (hand
movements) correspond with short musical structures known as sañcāras or motifs. Such units are musically
meaningful in Karnatak music, acting as building blocks of compositions and extemporizations (Viswanathan
1977). We assess our dataset for gestural catchments, seeking to discover whether either gestural spatial trajectory
or acceleration are most reliably connected with musical motifs. We do this to understand how co-singing gestures
index musically meaningful units.