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      Shared periodic performer movements coordinate interactions in duo improvisations

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          Abstract

          Human interaction involves the exchange of temporally coordinated, multimodal cues. Our work focused on interaction in the visual domain, using music performance as a case for analysis due to its temporally diverse and hierarchical structures. We made use of two improvising duo datasets—(i) performances of a jazz standard with a regular pulse and (ii) non-pulsed, free improvizations—to investigate whether human judgements of moments of interaction between co-performers are influenced by body movement coordination at multiple timescales. Bouts of interaction in the performances were manually annotated by experts and the performers’ movements were quantified using computer vision techniques. The annotated interaction bouts were then predicted using several quantitative movement and audio features. Over 80% of the interaction bouts were successfully predicted by a broadband measure of the energy of the cross-wavelet transform of the co-performers’ movements in non-pulsed duos. A more complex model, with multiple predictors that captured more specific, interacting features of the movements, was needed to explain a significant amount of variance in the pulsed duos. The methods developed here have key implications for future work on measuring visual coordination in musical ensemble performances, and can be easily adapted to other musical contexts, ensemble types and traditions.

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          Random forests

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            Application of the cross wavelet transform and wavelet coherence to geophysical time series

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              An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

              Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing. (c) 2009 APA, all rights reserved.
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                Author and article information

                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society Publishing
                2054-5703
                February 2018
                21 February 2018
                21 February 2018
                : 5
                : 2
                Affiliations
                [1 ]Department of Music, Durham University , Durham, UK
                [2 ]Reid School of Music, University of Edinburgh , Edinburgh, UK
                [3 ]MARCS Institute for Brain, Behaviour and Development, Western Sydney University , Sydney, New South Wales, Australia
                Author notes
                Author for correspondence: Tuomas Eerola e-mail: tuomas.eerola@ 123456durham.ac.uk

                Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.4005982.

                Article
                rsos171520
                10.1098/rsos.171520
                5830756
                © 2018 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                Product
                Funding
                Funded by: Arts and Humanities Research Council, http://dx.doi.org/10.13039/501100000267;
                Award ID: AH/N00308X/1
                Categories
                1001
                205
                42
                Psychology and Cognitive Neuroscience
                Research Article
                Custom metadata
                February, 2018

                performance, wavelet, interaction, coordination, entrainment, music

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