Abstract
Learning to play and perform violin is a complex task, that requires a high conscious control and coordination for the player. In this paper, our aim is to understand which technology and which motion features can be used to efficiently and effectively distinguish a professional performance from a student one trading off intrusiveness and accuracy. We collected and made freely available a dataset consisting of Motion Capture (MOCAP), Electromyography, Accelerometer, and Gyroscope (MYO), and Microsoft Kinect (KINECT) recordings of different violinists with different skills performing different exercises covering different pedagogical and technical aspects. We then engineered peculiar features starting from the different sources (MOCAP, MYO, and KINECT) and trained a data-driven classifier to distinguish among two different levels of violinist experience, namely Beginners and Experts. We then studied how much accuracy do we loose when, instead of using MOCAP data (the most intrusive and costly technology), MYO data (which is less intrusive than MOCAP), or the KINECT data (the less intrusive technology) are exploited. In accordance with the hierarchy present in the dataset, we study two different scenarios: extrapolation with respect to different exercises and violinists. Furthermore we study which features are the most predictive ones of the quality of a violinist to corroborate the significance of the results. Results, both in terms of accuracy and insight on the cognitive problem, support the proposal and support the use of the presented technique as an effective tool for students to monitor and enhance their home study and practice.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: International Workshop on Ambient Assisted Living (2012)
Aróstegui, J.L.: Educating Music Teachers for the 21st Century. Springer Science & Business Media, Rotterdam (2011). https://doi.org/10.1007/978-94-6091-503-1
Baader, A.P., Kazennikov, O., Wiesendanger, M.: Coordination of bowing and fingering in violin playing. Cogn. Brain Res. 23(2–3), 436–443 (2005)
Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: International Conference on Pervasive Computing (2004)
Barry, N.H.: The effects of practice strategies, individual differences in cognitive style, and gender upon technical accuracy and musicality of student instrumental performance. Psychol. Music 20(2), 112–123 (1992)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Butepage, J., Black, M.J., Kragic, D., Kjellstrom, H.: Deep representation learning for human motion prediction and classification. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)
Calude, C.S., Longo, G.: The deluge of spurious correlations in big data. Found. Sci. 22(3), 595–612 (2017)
Camurri, A., Coletta, P., Varni, G., Ghisio, S.: Developing multimodal interactive systems with EyesWeb XMI. In: International Conference on New Interfaces for Musical Expression (2007)
Cho, K., Chen, X.: Classifying and visualizing motion capture sequences using deep neural networks. In: International Conference on Computer Vision Theory and Applications (2014)
Dalmazzo, D., Ramírez, R.: Bow gesture classification to identify three different expertise levels: a machine learning approach. In: Cellier, P., Driessens, K. (eds.) ECML PKDD 2019. CCIS, vol. 1168, pp. 494–501. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43887-6_43
Dalmazzo, D., Ramírez, R.: Bowing gestures classification in violin performance: a machine learning approach. Front. Psychol. 10, 344 (2019)
Dalmazzo, D., Tassani, S., Ramírez, R.: A machine learning approach to violin bow technique classification: a comparison between IMU and MOCAP systems. In: International Workshop on Sensor-Based Activity Recognition and Interaction (2018)
D’Amato, V., Volta, E., Oneto, L., Volpe, G., Camurri, A., Anguita, D.: Understanding violin players’ skill level based on motion capture: a data-driven perspective. Cogn. Comput. 12(6), 1356–1369 (2020)
Davidson, J.W.: Visual perception of performance manner in the movements of solo musicians. Psychol. Music 21(2), 103–113 (1993)
DeVaul, R.W., Dunn, S.: Real-time motion classification for wearable computing applications. MIT Technical Report (2001)
Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15(1), 3133–3181 (2014)
Genuer, R., Poggi, J.M., Tuleau-Malot, C.: Variable selection using random forests. Pattern Recogn. Lett. 31(14), 2225–2236 (2010)
Goebl, W., Dixon, S., Schubert, E.: Quantitative methods: Motion analysis, audio analysis, and continuous response techniques. In: Expressiveness in Music Performance: Empirical Approaches Across Styles and Cultures, pp. 221–239 (2014)
Good, P.: Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. Springer, New York (2013). https://doi.org/10.1007/978-1-4757-2346-5
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)
Hallam, S.: The development of metacognition in musicians: implications for education. Br. J. Music Educ. 18(1), 27–39 (2001)
Jacobs, C.: Investigation of kinesthetics in violin playing. J. Res. Music Educ. 17(1), 112–114 (1969)
Magill, R., Anderson, D.: Motor Learning and Control. McGraw-Hill Publishing, New York (2010)
Marquez-Borbon, A.: Perceptual learning and the emergence of performer-instrument interactions with digital music systems. In: Proceedings of a Body of Knowledge - Embodied Cognition and the Arts conference (2018)
McPherson, G.E., Renwick, J.M.: A longitudinal study of self-regulation in children’s musical practice. Music Educ. Res. 3(2), 169–186 (2001)
Ng, K., Larkin, O., Koerselman, T., Ong, B.: i-maestro gesture and posture support: 3D motion data visualisation for music learning and playing. In: London International Conference (2007)
Oneto, L.: Model Selection and Error Estimation in a Nutshell. MOST, vol. 15. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-24359-3
Rosa-Pujazón, A., Barbancho, I., Tardón, L.J., Barbancho, A.M.: Fast-gesture recognition and classification using kinect: an application for a virtual reality drumkit. Multimedia Tools Appl. 75(14), 8137–8164 (2016)
Ruggieri, V., Katsnelson, A.: An analysis of a performance by the violinist D. Oistrakh: the hypothetical role of postural tonic-static and entourage movements. Percept. Mot. Skills 82(1), 291–300 (1996)
Saeys, Y., Abeel, T., Van de Peer, Y.: Robust feature selection using ensemble feature selection techniques. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS (LNAI), vol. 5212, pp. 313–325. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87481-2_21
Sama, A., Pardo-Ayala, D.E., Cabestany, J., Rodríguez-Molinero, A.: Time series analysis of inertial-body signals for the extraction of dynamic properties from human gait. In: International Joint Conference on Neural Networks (2010)
Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory To Algorithms. Cambridge University Press (2014)
Turner-Stokes, L., Reid, K.: Three-dimensional motion analysis of upper limb movement in the bowing arm of string-playing musicians. Clin. Biomech. 14(6), 426–433 (1999)
Visentin, P., Li, S., Tardif, G., Shan, G.: Unraveling mysteries of personal performance style; biomechanics of left-hand position changes (shifting) in violin performance. Peer J. 3, e1299 (2015)
Volta, E., Mancini, M., Varni, G., Volpe, G.: Automatically measuring biomechanical skills of violin performance: an exploratory study. In: International Conference on Movement and Computing (2018)
Wainberg, M., Alipanahi, B., Frey, B.J.: Are random forests truly the best classifiers? J. Mach. Learn. Res. 17(1), 3837–3841 (2016)
Wang, N., Ambikairajah, E., Lovell, N.H., Celler, B.G.: Accelerometry based classification of walking patterns using time-frequency analysis. In: IEEE Engineering in Medicine and Biology Society (2007)
Zlatintsi, A., et al.: In: A web-based real-time kinect application for gestural interaction with virtual musical instruments, pp. 1–6. Sound in Immersion and Emotion (2018)
Acknowledgments
This work is partially supported by the University of Genova through the Bando per l’incentivazione alla progettazione europea 2019 - Mission 1 “Promoting Competitiveness”, the EU-H2020-ICT Project TELMI (G.A. 688269), and the EU-H2020-FETPROACT Project ENTIMEMENT (G.A. 824160).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
D’Amato, V., Volta, E., Oneto, L., Volpe, G., Camurri, A., Anguita, D. (2021). Accuracy and Intrusiveness in Data-Driven Violin Players Skill Levels Prediction: MOCAP Against MYO Against KINECT. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12862. Springer, Cham. https://doi.org/10.1007/978-3-030-85099-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-85099-9_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85098-2
Online ISBN: 978-3-030-85099-9
eBook Packages: Computer ScienceComputer Science (R0)