ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

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  • E-ISSN 3022-5388

머신러닝을 활용한 코다이 학습장치의 인식률 변화

Changes in the Recognition Rate of Kodály Learning Devices using Machine Learning

한국인공지능학회지 / Journal of Korean Artificial Intelligence Association, (E)3022-5388
2024, v.2 no.1, pp.25-30
https://doi.org/10.24225/jkaia.2024.2.1.25
YunJeong LEE (Eulji University)
Dong Kun CHUNG (Eulji University)
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Abstract

Kodály hand signs are symbols that intuitively represent pitch and note names based on the shape and height of the hand. They are an excellent tool that can be easily expressed using the human body, making them highly engaging for children who are new to music. Traditional hand signs help beginners easily understand pitch and significantly aid in music learning and performance. However, Kodály hand signs have distinctive features, such as the ability to indicate key changes or chords using both hands and to clearly represent accidentals. These features enable the effective use of Kodály hand signs. In this paper, we aim to investigate the changes in recognition rates according to the complexity of scales by creating a device for learning Kodály hand signs, teaching simple Do-Re-Mi scales, and then gradually increasing the complexity of the scales and teaching complex scales and children's songs (such as "May Had A Little Lamb"). The learning device utilizes accelerometer and bending sensors. The accelerometer detects the tilt of the hand, while the bending sensor detects the degree of bending in the fingers. The utilized accelerometer is a 6-axis accelerometer that can also measure angular velocity, ensuring accurate data collection. The learning and performance evaluation of the Kodály learning device were conducted using Python.

keywords
Kodai hand symbol, Scale complexity, Recognition ratio, Artificial Intelligence, etc


투고일Submission Date
2024-04-15
수정일Revised Date
2024-06-14
게재확정일Accepted Date
2024-06-30
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