Floriane Dardard
Skip Abstract Section
Abstract
The aim of the present work is to analyze automatically the leading interactions between the musicians of a string quartet, using machine-learning techniques applied to nonverbal features of the musicians’ behavior, which are detected through the help of a motion-capture system. We represent these interactions by a graph of “influence” of the musicians, which displays the relations “is following” and “is not following” with weighted directed arcs. The goal of the machine-learning problem investigated is to assign weights to these arcs in an optimal way. Since only a subset of the available training examples are labeled, a semisupervised support vector machine is used, which is based on a linear kernel to limit its model complexity. Specific potential applications within the field of human-computer interaction are also discussed, such as e-learning, networked music performance, and social active listening.
- O. Bousquet and A. Elisseeff. 2002. Stability and generalization. Journal of Machine Learning Research 2, 499--526. Google Scholar
Digital Library
- C. Brunner, A. Fischer, K. Luig, and T. Thies. 2012. Pairwise support vector machines and their application to large scale problems. Journal of Machine Learning Research 13, 2279--2292. Google ScholarDigital Library
- A. Camurri, P. Coletta, G. Varni, and S. Ghisio. 2007. Developing multimodal interactive systems with EyesWeb XMI. In Proceedings of the 7th International Conference on New Interfaces for Musical Expression (NIME’07). ACM, New York, NY. Google ScholarDigital Library
- C. Cortes and M. Mohri. 2007. On transductive regression. In Advances in Neural Information Processing (NIPS), Vol. 19, B. Schölkopf, J. C. Platt, and T. Hoffman (Eds.). 305--312.Google Scholar
- N. Cristianini and J. Shawe-Taylor. 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, New York, NY. Google ScholarDigital Library
- S. Dahl, F. Bevilacqua, R. Bresin, M. Clayton, L. Leante, I. Poggi, and N. Rasamimanana. 2009. Gestures in performance. In Musical Gestures. Sound, Movement, and Meaning, M. Leman and R. I. Godoy (Eds.). Routledge, 36--68.Google Scholar
- J. W. Davidson. 1993. Visual perception of performance manner in the movements of solo musicians. Psychology of Music 21, 103--113.Google ScholarCross Ref
- S. Feese, B. Arnrich, G. Tröster, B. Meyer, and K. Jonas. 2012. Quantifying behavioral mimicry by automatic detection of nonverbal cues from body motion. In Proceedings of the 4th IEEE International Conference on Privacy, Security, Risk and Trust (PASSAT) and 4th IEEE International Conference on Social Computing (SOCIALCOM). IEEE, 520--525. Google ScholarDigital Library
- A. Gilboa and M. Tal-Shmotkin. 2012. String quartets as self-managed teams: An interdisciplinary perspective. Psychology of Music 40, 19--41.Google ScholarCross Ref
- D. Glowinski, L. Badino, A. D’Ausilio, A. Camurri, and L. Fadiga. 2012. Analysis of leadership in a string quartet. In Proceedings of the 3rd Workshop on Social Behaviour in Music (SBM) at the ACM International Conference on Multimodal Interaction (ICMI’12). ACM, New York, NY. 6 pages.Google Scholar
- D. Glowinski, F. Dardard, G. Gnecco, S. Piana, and A. Camurri. 2015. Expressive non-verbal interaction in a string quartet: An analysis through head movements. Journal on Multimodal User Interfaces 9, 55--68. DOI:10.1007/s12193-014-0154-3.Google ScholarCross Ref
- A. Hutchinson. 1974. Labanotation. Oxford University Press, Oxford, UK.Google Scholar
- D. B. Jayagopi and D. Gatica-Perez. 2009. Discovering group nonverbal conversational patterns with topics. In Proceedings of the International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI’09). ACM, New York, NY, 3--6. Google ScholarDigital Library
- K. Khoshhal, H. Aliakbarpour, J. Quintas, P. Drews, and J. Dias. 2010. Probabilistic LMA-based classification of human behaviour understanding using power spectrum technique. In Proceedings of the 13th International Conference on Information Fusion (EICC’10). IEEE, 1--7.Google Scholar
- K. Khoshhal, L. Santos, H. Aliakbarpour, and J. Dias. 2012. Parameterizing interpersonal behaviour with Laban movement analysis—Bayesian approach. In Proceedings of the 3rd International Workshop on Socially Intelligent Surveillance and Monitoring (SISM’12) in CVPR’12. IEEE, 7--13.Google Scholar
- T. Kim, A. Chang, L. Holland, and A. S. Pentland. 2008. Meeting mediator: Enhancing group collaboration using sociometric feedback. In Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work. ACM, New York, NY, 457--466. Google ScholarDigital Library
- R. Laban and L. Ullmann. 1971. The Mastery of Movement. Dance Books Limited, Binsted, Hampstead, UK.Google Scholar
- O. Madani, D. M. Pennock, and G. W. Flake. 2004. Co-Validation: Using model disagreement on unlabeled data to validate classification algorithms. In Proceedings of the 17th International Conference on Neural Information Processing Systems (NIPS’04). 873--880.Google Scholar
- P. P. Maglio, T. Matlock, C. S. Campbell, S. Zhai, and B. A. Smith. 2000. Gaze and speech in attentive user interfaces. In Advances in Multimodal Interfaces, Proceedings of the International Conference on Multimodal Interfaces (ICMI’00). Springer, Berlin, 1--7. Google ScholarDigital Library
- T. Opsahla, F. Agneessensb, and J. Skvoretzc. 2010. Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks 35, 245--251.Google ScholarCross Ref
- R. Q. Quiroga, T. Kreuz, and P. Grassberger. 2002. Event synchronization: A simple and fast method to measure synchronicity and time delay patterns. Physical Review E 66, 41904--41909.Google ScholarCross Ref
- J. Rett. 2008. Robot-Human interface using Laban movement analysis inside a Bayesian framework. PhD dissertation. University of Coimbra, Coimbra, Portugal.Google Scholar
- D. Sanchez-Cortes, O. Aran, M. S. Mast, and D. Gatica-Perez. 2010. Identifying emergent leadership in small groups using nonverbal communicative cues. In Proceedings of the International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI’10). ACM, New York, NY. Article number 39, 4 pages. Google ScholarDigital Library
- D. Sanchez-Cortes, O. Aran, M. S. Mast, and D. Gatica-Perez. 2012. A nonverbal behavior approach to identify emergent leaders in small groups. IEEE Transactions on Multimedia 14, 816--832. Google ScholarDigital Library
- L. Santos and J. Dias. 2009. Human-robot interaction: Invariant 3-D features for Laban movement analysis shape component. In Proceedings of the 14th IASTED International Conference on Robotics and Applications (RA’09). 255--262.Google Scholar
- V. Sindhwani and S. S. Keerthi. 2006. Large scale semi-supervised linear SVMs. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, 477--484. Software available at http://vikas.sindhwani.org/svmlin.html. Google ScholarDigital Library
- R. Stiefelhagen. 2002. Tracking focus of attention in meetings. In Proceedings of the 4th IEEE International Conference on Multimodal Interfaces. IEEE, 273--280. Google ScholarDigital Library
- V. Vapnik. 2000. The Nature of Statistical Learning Theory. Springer, New York, NY. Google ScholarDigital Library
- G. Varni, M. Mancini, G. Volpe, and A. Camurri. 2011a. A system for mobile active music listening based on social interaction and embodiment. Mobile Networks and Applications 16, 375--384. Google ScholarDigital Library
- G. Varni, G. Volpe, and B. Mazzarino. 2011b. Towards a social retrieval of music content. In Proceedings of the 3rd IEEE International Conference on Privacy, Security, Risk and Trust (PASSAT) and 3rd IEEE International Conference on Social Computing (SOCIALCOM). IEEE, 1466--1473.Google Scholar
- L. Zhao. 2002. Synthesis and Acquisition of Laban Movement Analysis Qualitative Parameters for Communicative Gestures. PhD dissertation. University of Pennsylvania, Philadelphia, PA. Google ScholarDigital Library
- L. Zhao and N. I. Badler. 2005. A nonverbal behavior approach to identify emergent leaders in small groups. Graphical Models 67, 1--16.Google ScholarDigital Library
- X. Zhu and A. B. Goldberg. 2009. Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers, San Francisco, CA. Google ScholarDigital Library
Index Terms
- Automatic Classification of Leading Interactions in a String Quartet
Recommendations
Twin Support Vector Machines for Pattern Classification
We propose Twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The Twin SVM formulation is in the spirit of proximal SVMs via generalized ...
Extending twin support vector machine classifier for multi-category classification problems
Twin support vector machine classifier TWSVM was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification problems, ...
Fuzzy support vector machines for multilabel classification
The problem of one-against-all support vector machines (SVMs) for multilabel classification is that a data sample may be classified into a multilabel class that is not defined or it may not be classified into any class. To solve this problem, in this ...
Comments