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Violin Timbre Navigator: Real-Time Visual Feedback of Violin Bowing Based on Audio Analysis and Machine Learning

  • Alfonso Perez-Carrillo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)

Abstract

Bowing is the main control mechanism in sound production during a violin performance. The balance among bowing parameters such as acceleration, force, velocity or bow-bridge distance are continuously determining the characteristics of the sound. However, in traditional music pedagogy, approaches to teaching the mechanics of bowing are based on subjective and vague perception, rather than on accurate understanding of the principles of movement bowing. In the last years, advances in technology has allowed to measure bowing parameters in violin performances. However, sensing systems are generally very expensive, intrusive and require for very complex and time consuming setups, which makes it impossible to bring them into a classroom environment. Here, we propose an algorithm that is able to estimate bowing parameters from audio analysis in real-time, requiring just a microphone and a simple calibration process. Additionally, we present the Violin Palette, a prototype that uses the reported algorithm and presents bowing information in an intuitive way.

Keywords

Audio analysis Violin bowing Machine learning 

References

  1. 1.
    Askenfelt, A.: Measurement of bow motion and bow force in violin playing. J. Acoust. Soc. Am. 80(4), 1007–1015 (1986).  https://doi.org/10.1121/1.393841CrossRefGoogle Scholar
  2. 2.
    Askenfelt, A.: Measurement of the bowing parameters in violin playing II: bow-bridge distance, dynamic range, and limits of bow force. J. Acoust. Soc. Am. 86(2), 503–516 (1989).  https://doi.org/10.1121/1.398230CrossRefGoogle Scholar
  3. 3.
    Brandfonbrener, A.G.: Musculoskeletal problems of instrumental musicians. Hand Clin. 19(2), 231–239 (2003)CrossRefGoogle Scholar
  4. 4.
    Cremer, L.: Physics of the Violin. The MIT Press, Cambridge (1984)Google Scholar
  5. 5.
    Demoucron, M.: On the control of virtual violins: Physical modelling and control of bowed string instruments. Ph.D. thesis, Universite Pierre et Marie Curie (Paris, France) and the Stockholm Royal Institute of Technology (Stockholm, Sweden) (2008)Google Scholar
  6. 6.
    Guaus, E., Bonada, J., Maestre, E., Perez, A., Blaauw, M.: Calibration method to measure accurate bow force for real violin performances. In: International Computer Music Conference, Montreal, Canada, pp. 251–254, August 2009Google Scholar
  7. 7.
    Guettler, K., Askenfelt, A.: On the creation of the Helmholtz motion in bowed strings. Acust. Acta Acust. 88(6), 970–985 (2002)Google Scholar
  8. 8.
    Ho, T.K.: Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, vol. 14-16, pp. 278–282, August 1995Google Scholar
  9. 9.
    Kristis, K., Pérez-Carrillo, A.: Gesture recognition for musiclearning assesment. Master’s thesis, Universitat Pompeu Fabra, Barcelona, Spain (2016)Google Scholar
  10. 10.
    Maestre, E., et al.: Enriched multimodal representations of music performances: online access and visualization. IEEE MultiMedia Mag. 24(1), 24–34 (2017)CrossRefGoogle Scholar
  11. 11.
    Maestre, E., Bonada, J., Blaauw, M., Pérez, A., Guaus, E.: Acquisition of violin instrumental gestures using a commercial EMF device. In: International Computer Music Conference, Copenhagen, Denmark (2007)Google Scholar
  12. 12.
    Peeters, G.: A large set of audio features for sound description (similarity and classification) in the cuidado project. Technical report, IRCAM, Paris, France (2004)Google Scholar
  13. 13.
    Pérez, A., Wanderley, M.M.: Indirect acquisition of violin instrumental controls from audio signal with Hidden Markov models. IEEE/ACM Trans. Audio Speech Lang. Process. 23(5), 932–940 (2015).  https://doi.org/10.1109/TASLP.2015.2410140CrossRefGoogle Scholar
  14. 14.
    Perez-Carrillo, A., Bonada, J., Maestre, E., Guaus, E., Blaauw, M.: Performance control driven violin timbre model based on neural networks. IEEE Trans. Audio Speech Lang. Process. 20(3), 1007–1021 (2012).  https://doi.org/10.1109/TASL.2011.2170970CrossRefGoogle Scholar
  15. 15.
    Perez-Carrillo, A.: Enhancing spectral synthesis techniques with performance gestures using the violin as a case study. Ph.D. thesis, Universitat Pompeu Fabra (2009). http://www.mtg.upf.edu/static/media/Perez-Alfonso-PhD-2009.pdf
  16. 16.
    Perez-Carrillo, A.: Statistical models for the indirect acquisition of violin bowing controls from audio analysis. In: Proceedings of Meetings on Acoustics 172ASA. vol. 29, p. 035003. ASA (2016)Google Scholar
  17. 17.
    Pérez-Carrillo, A., Wanderley, M.: Learning and extraction of violin instrumental controls from audio signal. In: In proc. of the MIRUM Workshop, ACM Multimedia Conference, Nara, Japan, November 2012Google Scholar
  18. 18.
    Rasamimanana, N.: Gesture analysis of bow strokes using an augmented violin. Master’s thesis, IRCAM, Paris, France (2003)Google Scholar
  19. 19.
    Schelleng, J.: The bowed string and the player. J. Acoust. Soc. Am. 53(1), 26–41 (1973)CrossRefGoogle Scholar
  20. 20.
    Schoner, B.: Probabilistic characterization and synthesis of complex driven systems. Ph.D. thesis, MIT Media Lab, Cambridge, Massachusetts, USA (2000)Google Scholar
  21. 21.
    Schoonderwaldt, E., Demoucron, M.: Extraction of bowing parameters from violin performance combining motion capture and sensors. J. Acoust. Soc. Am. 126(5), 2695–2708 (2009).  https://doi.org/10.1121/1.3227640. http://link.aip.org/link/?JAS/126/2695/1CrossRefGoogle Scholar
  22. 22.
    Wanderley, M.M., Depalle, P.: Gestural control of sound synthesis. In: Proceedings of the IEEE, pp. 632–644 (2004)Google Scholar
  23. 23.
    Welch, G.F.: Variability of practice and knowledge of results as factors in learning to sing in tune. In: Bulletin of the Council for Research in Music Education, pp. 238–247 (1985)Google Scholar
  24. 24.
    Young, D.S.: Wireless sensor system for measurement of violin bowing parameters. In: Proceedings of the Stockholm Music Acoustics Conference, Stockholm, Sweden (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Music Technology Group, Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain

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