Violin Fingering Estimation According to the Performer’s Skill Level Based on Conditional Random Field

  • Shinji SakoEmail author
  • Wakana Nagata
  • Tadashi Kitamura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9170)


In this paper, we propose a method that estimates appropriate violin fingering according to the performer’s skill level based on a conditional random field (CRF). A violin is an instrument that can produce the same pitch for different fingering patterns, and these patterns depend on skill level. We previously proposed a statistical method for violin fingering estimation, but that method required a certain amount of training data in the form of fingering annotation corresponding to each note in the music score. This was a major issue of our previous method, because it takes time and effort to produce the annotations. To solve this problem, we proposed a method to automatically generate training data for a fingering model using existing violin textbooks. Our experimental results confirmed the effectiveness of the proposed method.



This research was supported in part by Japan Society for the Promotion of Science (JSPS) KAKENHI (Grant-in-Aid for Scientific Research) Grant Number 26730182, and The Telecommunications Advancement Foundation.


  1. 1.
    Radisavljevic, A., Driessen, P.: Path difference learning for guitar fingering problem. In: Proceedings of the International Computer Music Conference, pp. 456–461 (2004)Google Scholar
  2. 2.
    Miura, M., Hirota, I., Hama, N., Yanagida, M.: Constructing a system for finger-position determination and tablature generation for playing melodies on guitars. Syst. Comput. Jpn 35(6), 10–19 (2004)CrossRefGoogle Scholar
  3. 3.
    Tuohy, D.R., Potter, W.D.: A genetic algorithm for the automatic generation of playable guitar tablature. In: Proc. the International Computer Music Conference, pp. 499–502 (2005)Google Scholar
  4. 4.
    Radicioni, D., Scienza, C.D., Lombardo, V.: Guitar fingering for music performance. In: Proc. the International Computer Music Conference. (2005) 527–530Google Scholar
  5. 5.
    Hori, G., Kameoka, H., Sagayama, S.: Input-output hmm applied to automatic arrangement for guitars. Journal of information processing 21(2), 264–271 (2013)CrossRefGoogle Scholar
  6. 6.
    Hart, M., Bosch, R., Tsia, E.: Finding optimal piano fingerings. The UMAP Journal 21(2), 167–177 (2000)Google Scholar
  7. 7.
    Yonebayashi, Y., Kameoka, H., Sagayama, S.: Automatic decision of piano fingering based on hidden markov models. In: Proc. the 20th International Joint Conference on Artificial Intelligence. (2007) 2915–2921Google Scholar
  8. 8.
    Kasimi, A.A., Nichols, E., Raphael, C.: A simple algorithm for automatic generation of polyphonic piano fingerings. In: Proc. the 8th International Conference on Music Information Retrieval. (2007) 355–356Google Scholar
  9. 9.
    Nagata, W., Sako, S., Kitamura, T.: Violin fingering estimation according to skill level based on hidden markov model. In: International Computer Music Conference (ICMC) and Sound and Music Computing conference (SMC) 2014. (2014) 1233–1238Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Nagoya Institute of TechnologyNagoyaJapan

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