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Expressive non-verbal interaction in a string quartet: an analysis through head movements

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The present study investigates expressive non-verbal interaction in the musical context starting from behavioral features extracted at individual and group levels. Four groups of features are defined, which are related to head movement and direction, and may help gaining insight on the expressivity and cohesion of the performance, discriminating between different performance conditions. Then, the features are evaluated both at a global scale and at a local scale. The findings obtained from the analysis of a string quartet recorded in an ecological setting show that using these features alone or in their combination may help in distinguishing between two types of performance: (a) a concert-like condition, where all musicians aim at performing at best, (b) a perturbed one, where the 1\(\mathrm{st}\) violinist devises alternative interpretations of the music score without discussing them with the other musicians. In the global data analysis, the discriminative power of the features is investigated through statistical tests. Then, in the local data analysis, a larger amount of data is used to exploit more sophisticated machine learning techniques to select suitable subsets of the features, which are then used to train an SVM classifier to perform binary classification. Interestingly, the features whose discriminative power is evaluated as large (respectively, small) in the global analysis are also evaluated in a similar way in the local analysis. When used together, the 22 features that have been defined in the paper demonstrate to be efficient for classification, leading to a percentage of about 90 % successfully classified examples among the ones not used in the training phase. Similar results are obtained considering only a subset of 15 features.

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  5. For what concerns the definitions of some features, we have corrected a typo present in [5], which reported an old definition of the features \(F_1\) and \(\mathbf{F}_3\) (given in terms of means over the frames instead than medians over the frames, as done in the present manuscript), although its numerical results about such features were actually obtained according to the same definitions of the present manuscript.

  6. That condition was mentioned in Sect. 3.1 only to describe how the procedure could be modified in the unlikely case such a condition would occur.

  7. One can notice that there is no contradiction about the use of the median inside the definition of the feature \(F_1\) in Sect. 3.1, and the use of the mean instead in the analysis described in this subsection. Indeed, the median among the frames of each recording was used in the definition of the feature \(F_1\) in Sect. 3.1, whereas the mean in this subsection was computed at another level of the analysis, i.e., averaging the obtained values of the feature \(F_1\) with respect to the recordings associated with the same performance condition. A similar remark holds for the feature \(\mathbf{F}_2\).

  8. The interonset interval (\(IoI\)) is the lapse of time between the beginnings of two consecutive time windows. Since in this work it was chosen to be smaller than the length of the time windows, consecutive time windows always overlapped. Although this introduced an additional correlation between the features computed on different time windows, this was limited to consecutive time windows.


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The project SIEMPRE acknowledges the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET-Open Grant Number: 250026-2.

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Correspondence to Donald Glowinski.

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Glowinski, D., Dardard, F., Gnecco, G. et al. Expressive non-verbal interaction in a string quartet: an analysis through head movements. J Multimodal User Interfaces 9, 55–68 (2015).

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