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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.7
- 수록면
- 1,098 - 1,101 (4page)
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초록· 키워드
오류제보하기
With the development of computer music notation programs, when drawing sheet music, it is often drawn using a computer. However, there are still many use of hand-written notations for educational purposes or to quickly draw sheet music such as listening and dictating. In previous studies, OMR focused on recognizing the printed music sheet made by music notation program. the result of handwritten OMR with camera is poor because different people have different writing methods, and lens distortion. In this study, as a pre-processing process for recognizing handwritten music sheet, we propose a method for recognizing a staff using linear regression and a method for recognizing a bar using CNN. F₁ scores of staff recognition and barline detection are 99.09% and 95.48%, respectively. This methodologies are expected to contribute to improving the accuracy of handwriting.
목차
ABSTRACT
Ⅰ. 서론
Ⅱ. 손사보 광학음악 인식
Ⅲ. 보표인식
Ⅳ. 마디인식
Ⅴ. 결론
REFERENCES
참고문헌 (7)
참고문헌 신청
A. Pacha, K. -Y. Choi, B. Cou ̈asnon, Y. Ricquebourg, R. Zanibbi, and H. Eidenberger, “Handwritten Music Object Detection: Open Issues and Baseline Results,” in Proceeding of 13th IAPR International Workshop on Document Analysis Systems, Vienna, Austria, pp. 163-168, 2018. DOI: 10.1109/DAS.2018.51.
F. Alirezazadeh and M. R. Ahmadzadeh, “Effective staff line detection, restoration and removal approachfor different quality of scanned handwritten music sheets,” Journal of Advanced Computer Science & Technology, vol. 3, no. 2, pp. 136-142, Jun. 2014. DOI: 10.14419/jacst.v3i2.3196.
A. J. Gallego and J. Callvo-Zaragoza “Staff-line Removal with Selectional Auto-Encoders,” Expert Systems with Applications, vol. 89, pp. 138-148, Dec. 2017. DOI: https://doi.org/10.1016/j.eswa.2017.07.002.
F. J. Castellanos, J. Calvo-Zaragoza, G. Vigliensoni, and I. Fujinaga, “Document Analysis of Music Score Images with Selectional Auto-encoders,” in Proceeding of the 19th International Society for Music Information Retrieval Conference, Paris, France, pp. 256-263, 2018. DOI: 10.5281/zenodo.1492397.
A. Fornés, A. Dutta, A. Gordo, and J. Lladós, “CVC-MUSCIMA: A Ground-truth of Handwritten Music Score Images for Writer Identification and Staff Removal,” International Journal on Document Analysis and Recognition, vol. 15, no. 3, pp. 243-251, Jun. 2012. DOI: 10.1007/s10032-011-0168-2.
F. Alirezazadeh and M. R. Ahmadzadeh, “Effective staff line detection, restoration and removal approachfor different quality of scanned handwritten music sheets,” Journal of Advanced Computer Science & Technology, vol. 3, no. 2, pp. 136-142, Jun. 2014. DOI: 10.14419/jacst.v3i2.3196.
A. J. Gallego and J. Callvo-Zaragoza “Staff-line Removal with Selectional Auto-Encoders,” Expert Systems with Applications, vol. 89, pp. 138-148, Dec. 2017. DOI: https://doi.org/10.1016/j.eswa.2017.07.002.
F. J. Castellanos, J. Calvo-Zaragoza, G. Vigliensoni, and I. Fujinaga, “Document Analysis of Music Score Images with Selectional Auto-encoders,” in Proceeding of the 19th International Society for Music Information Retrieval Conference, Paris, France, pp. 256-263, 2018. DOI: 10.5281/zenodo.1492397.
A. Fornés, A. Dutta, A. Gordo, and J. Lladós, “CVC-MUSCIMA: A Ground-truth of Handwritten Music Score Images for Writer Identification and Staff Removal,” International Journal on Document Analysis and Recognition, vol. 15, no. 3, pp. 243-251, Jun. 2012. DOI: 10.1007/s10032-011-0168-2.
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