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Measuring ensemble interdependence in a string quartet through analysis of multidimensional performance data

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dc.contributor.author Papiotis, Panagiotis, 1985-
dc.contributor.author Marchini, Marco, 1984-
dc.contributor.author Pérez Carrillo, Alfonso Antonio, 1977-
dc.contributor.author Maestre Gómez, Esteban
dc.date.accessioned 2019-05-13T14:24:50Z
dc.date.available 2019-05-13T14:24:50Z
dc.date.issued 2014
dc.identifier.citation Papiotis P, Marchini M, Perez-Carrillo A, Maestre E. Measuring ensemble interdependence in a string quartet through analysis of multidimensional performance data. Front Psychol. 2014;5:963. DOI: 10.3389/fpsyg.2014.00963
dc.identifier.issn 1664-1078
dc.identifier.uri http://hdl.handle.net/10230/37219
dc.description.abstract In a musical ensemble such as a string quartet, the musicians interact and influence each other's actions in several aspects of the performance simultaneously in order to achieve a common aesthetic goal. In this article, we present and evaluate a computational approach for measuring the degree to which these interactions exist in a given performance. We recorded a number of string quartet exercises under two experimental conditions (solo and ensemble), acquiring both audio and bowing motion data. Numerical features in the form of time series were extracted from the data as performance descriptors representative of four distinct dimensions of the performance: Intonation, Dynamics, Timbre, and Tempo. Four different interdependence estimation methods (two linear and two nonlinear) were applied to the extracted features in order to assess the overall level of interdependence between the four musicians. The obtained results suggest that it is possible to correctly discriminate between the two experimental conditions by quantifying interdependence between the musicians in each of the studied performance dimensions; the nonlinear methods appear to perform best for most of the numerical features tested. Moreover, by using the solo recordings as a reference to which the ensemble recordings are contrasted, it is feasible to compare the amount of interdependence that is established between the musicians in a given performance dimension across all exercises, and relate the results to the underlying goal of the exercise. We discuss our findings in the context of ensemble performance research, the current limitations of our approach, and the ways in which it can be expanded and consolidated.
dc.description.sponsorship This work was partially supported by the EU FP7 FET-Open SIEMPRE Project no. FP7-ICT-2009-C-250026, the Catalan Research Funding Agency AGAUR, Beatriu de Pinos Grant 2010 BP-A 00209, and FP7 Marie Curie Grant no. PIOF-GA-2012-331270. We would like to thank Erika Donald and Marcelo Wanderley, as well as Carolina Brum Medeiros, Vincent Freour, and Marcello Giordano for the support in organizing the experiment, and the CIRMMT and BRAMS labs at Montreal, Quebec, Canada for hosting them. Finally, we would like to thank the reviewers for their thoughtful comments as well as for pointing out errors/omissions in the manuscript.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Frontiers
dc.relation.ispartof Frontiers in psychology. 2014;5:963.
dc.rights © 2014 Papiotis, Marchini, Perez-Carrillo and Maestre. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title Measuring ensemble interdependence in a string quartet through analysis of multidimensional performance data
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3389/fpsyg.2014.00963
dc.subject.keyword Interdependence
dc.subject.keyword String quartet
dc.subject.keyword Ensemble performance
dc.subject.keyword Signal processing
dc.subject.keyword Motion capture
dc.subject.keyword Mutual information
dc.subject.keyword Nonlinear coupling
dc.subject.keyword Granger causality
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/250026
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/331270
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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