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Measuring social interaction in music ensembles
Author(s) -
Gualtiero Volpe,
Alessandro D’Ausilio,
Leonardo Badino,
Antonio Camurri,
Luciano Fadiga
Publication year - 2016
Publication title -
philosophical transactions of the royal society b biological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.753
H-Index - 272
eISSN - 1471-2970
pISSN - 0962-8436
DOI - 10.1098/rstb.2015.0377
Subject(s) - variety (cybernetics) , computer science , focus (optics) , cognitive science , task (project management) , string (physics) , ideal (ethics) , cognitive psychology , data science , psychology , artificial intelligence , epistemology , mathematics , physics , engineering , systems engineering , optics , philosophy , mathematical physics
Music ensembles are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music ensembles offer a broad variety of scenarios which are particularly suitable for investigation. Small ensembles, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger ensembles, such as orchestras, the relationship between a leader (the conductor) and a group of followers (the musicians) clearly emerges. This paper presents an overview of recent research on social interaction in music ensembles with a particular focus on (i) studies from cognitive neuroscience; and (ii) studies adopting a computational approach for carrying out automatic quantitative analysis of ensemble music performances.

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