DOI QR코드

DOI QR Code

A Method for Measuring the Difficulty of Music Scores

  • Song, Yang-Eui (Dept. of Computer Science and Engineering, Dongguk University) ;
  • Lee, Yong Kyu (Dept. of Computer Science and Engineering, Dongguk University)
  • Received : 2016.01.04
  • Accepted : 2016.04.18
  • Published : 2016.04.29

Abstract

While the difficulty of the music can be classified by a variety of standard, conventional methods are classified by the subjective judgment based on the experience of many musicians or conductors. Music score is difficult to evaluate as there is no quantitative criterion to determine the degree of difficulty. In this paper, we propose a new classification method for determining the degree of difficulty of the music. In order to determine the degree of difficulty, we convert the score, which is expressed as a traditional music score, into electronic music sheet. Moreover, we calculate information about the elements needed to play sheet music by distance of notes, tempo, and quantifying the ease of interpretation. Calculating a degree of difficulty of the entire music via the numerical data, we suggest the difficulty evaluation of the score, and show the difficulty of music through experiments.

Keywords

References

  1. Su Mi Kwon and Wan Kyu Chung, "A comparative study of music grading system operated by ABRSM, NYSSMA and the national association of private music institutions in South Korea.", Vol. 43, No. 4, pp. 25-55, Korean Journal of Research in Music Education, 2014.
  2. Dan ha Kim, "A Search for Improving the Korea Piano Level Evaluation System through Comparisons of Piano Evaluation Criteria of England, Canada and China", Ph. D. Dissertation, Hansei University, 2014.
  3. Sheet Music Plus, SMP Level Guidelines, http://www.sheetmusicplus.com/help/level-guidelines
  4. ABRSM, Exam Information & Regulations 2015, http://www.abrsm.org
  5. NYSSMA, MYSSMA Manual, https://www.nyssma.org
  6. McKinney, Martin F., and Jeroen Breebaart. "Features for audio and music classification." ISMIR. Vol. 3. 2003.
  7. Basili, Roberto, Alfredo Serafini, and Armando Stellato. "Classification of musical genre: a machine learning approach." ISMIR. 2004.
  8. N. Scaringella, G. Zoia and D. Mlynek, "Automatic genre classification of music content: a survey," in IEEE Signal Processing Magazine, vol. 23, no. 2, pp. 133-141, March 2006.
  9. K. Yoon, J. Lee and M. U. Kim, "Music recommendation system using emotion triggering low-level features," in IEEE Transactions on Consumer Electronics, vol. 58, no. 2, pp. 612-618, May 2012.
  10. Sebastien, Veronique, et al. "Score Analyzer: Automatically Determining Scores Difficulty Level for Instrumental e-Learning." ISMIR. 2012.
  11. Najeeb Ullah Khan and Jung-Chul Lee, "Development of a Music Score Editor based on MusicXML", Journal of the Korea Society of Computer and Information, Vol. 19, No. 2, pp. 77-90, Feb. 2014. https://doi.org/10.9708/jksci.2014.19.2.077
  12. Good, Michael. "MusicXML: An internet-friendly format for sheet music." XML Conference and Expo. 2001.
  13. MusicXML Specification, http://www.musicxml.com