Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis technique to support personalized music education based on learner and chord data

맞춤형 음악 교육을 지원하기 위한 학습자 및 코드 데이터 분석 기법

  • Jung, Woosung (Graduate School of Education, Seoul National University of Education) ;
  • Lee, Eunjoo (School of Computer Science and Engineering, Kyungpook National University)
  • 정우성 (서울교육대학교 교육전문대학원) ;
  • 이은주 (경북대학교 컴퓨터학부)
  • Received : 2021.01.15
  • Accepted : 2021.02.20
  • Published : 2021.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Due to the growth of digital media technology, there is increasing demand of personalized education based on context data of learners throughout overall education area. For music education, several studies have been conducted for providing appropriate educational contents to learners by considering some factors such as the proficiency, the amount of practice, and their capability. In this paper, a technique has been defined to recommend the appropriate music scores to learners by extracting and analyzing the practice data and chord data. Concretely, several meaningful relationships among chords patterns and learners were analyzed and visualized by constructing the learners' profiles of proficiency, extracting the chord sequences from music scores. In addition, we showed the potential for use in personalized education by analyzing music similarity, learner's proficiency similarity, learner's proficiency of music and chord, mastered chords and chords sequence patterns. After that, the chord practice programs can be effectively generated considering various music scores using the synthetically summarized chord sequence graphs for the music scores that the learners selected.

디지털 미디어와 처리기술의 발전으로 여러 교육 분야에서 학습자의 문맥 데이터에 기반한 맞춤형 교육의 수요가 증가하고 있다. 음악 교육에서도 학습자의 숙련도와 연습량, 능력치 등을 고려하여 학습자에게 적합한 교육 컨텐츠를 제공해주는 연구가 진행되고 있다. 본 논문에서는 코드를 습득하려는 학습자에게 적합한 악보를 추천하는데 도움을 주기 위하여 연습 데이터와 악보의 코드 데이터를 추출하고 분석하는 기법을 정의하였다. 구체적으로, 학습자의 숙련도 프로파일을 구축하고, 악보로부터 코드 시퀀스 패턴을 추출하여 이들의 관계를 분석하고 시각화하였다. 그리고 악보 유사도, 학습자의 숙련도 유사도, 학습자의 악보 및 코드 숙련도, 학습자별 마스터된 코드와 코드 시퀀스 패턴에 대한 분석을 수행하여 맞춤형 교육에 활용 가능성을 보였다. 이후 사용자가 선택한 악보 리스트를 대상으로 통합 요약된 코드 시퀀스 그래프를 생성하면 여러 악보를 동시에 고려한 코드 연습 프로그램을 효과적으로 만들 수 있다.

Keywords

References

  1. J. Park, D. Kim, J. Kim & M. Song. (2020). Design and Development of Adaptive Online Learning Management System for Harmony. Journal of the Korea Convergence Society, 11(8). 139-145. DOI: 10.15207/JKCS.2020.11.8.139
  2. W. Jung. (2019). A Genetic Algorithm Based Learning Path Optimization for Music Education. Journal of the Korea Convergence Society. 10(2). 13-20. DOI: 10.15207/JKCS.2019.10.2.013
  3. W. Jung. (2020). A Design for the Personalized Difficulty Level Metric based on Learning State. Journal of the Korea Convergence Society. 11(3). 67-75. DOI: 10.15207/JKCS.2020.11.3.067
  4. E. Nichols, D. Morris & S. Basu. (2009). Data-driven Exploration of Musical Chord Sequences. International Conference on Intelligent User Interfaces. (pp.227-236).
  5. S. Koelsch. (2005). Investigating emotion with music: neuroscientific approaches. Annals of the New York Academy of Science. 412-418. DOI: 10.1196/annals.1360.034.
  6. S. Kurabayashi & T. Imai. (2014), Chord-Cube: Music Visualization and Navigation System with an Emotion-Aware Metric Space for Temporal Chord Progression, International Journal on Advances in Internet Technology, 1(7), 52-62.
  7. R. Typke, F. Wiering & R.C. Veltkamp. (2005). A Survey of Music Information Retrieval Systems. International Conference on Music Information Retrieval.
  8. R. Khulusi, J. Kusnick, C. Meinecke, J. Focht & S. Janicke. (2020), A Survey on Visualization for Musical Data, Computer Graphics Forum, 39(6), 82-110. DOI: 10.1111/cgf.13905.
  9. L. Bigo, M. Andreatta, J. Giavitto, O. Michel & A. Spicher. (2020), Computation and Visualization of Musical Structures in Chord-based Simplicial Complexes. International Conference on Mathematics and Computation in Music. (pp.38-51).
  10. P. Lammens. (2018) Analyzing and Visualizing Chord Sequences in Music. https://community.wolfram.com/groups/-/m/t/1383630
  11. L. Marsik. (2017). Student Research Abstract: Using Chord Distance Descriptors to Enhance Music Information Retrieval. The Symposium on Applied Computing. (pp.963-964).
  12. P. Knees & M. Schedl. (2015). Music Retrieval and Recommendation: A Tutorial Overview. International ACMSIGIR Conference on Research and Development in Information Retrieval. (pp.1133-1136).
  13. J Pauwels & M.B. Sandler. (2019), A Web-Based System for Suggesting New Practice Material to Music Learners Based on Chord Content, Joint Proceedings of the ACM IUI 2019 Workshops.
  14. J. Pauwels, K. O'Hanlon, G. Fazckas & M. B. Sandler. (2017). Confidence Measures and Their Applications in Music Labelling Systems Based on Hidden Markov Models. Conference of the International Society for Music Information Retrieval. (pp.279-285). Singapore.
  15. J. Pawels, G. Fazekans & M. B. Sandler. (2018). Recommending songs to music learners based on chord content. Joint Workshop on Machine Learning for Music. Stockholmsmassan.