TY - JOUR
T1 - Musicologist-driven writer identification in early music manuscripts
AU - Niitsuma, Masahiro
AU - Schomaker, Lambert
AU - Oosten, Jean Paul van
AU - Tomita, Yo
AU - Bell, David
N1 - Publisher Copyright:
© 2015, Springer Science+Business Media New York.
PY - 2016/6/1
Y1 - 2016/6/1
N2 - Recent renewed interest in computational writer identification has resulted in an increased number of publications. In relation to historical musicology its application has so far been limited. One of the obstacles seems to be that the clarity of the images from the scans available for computational analysis is often not sufficient. In this paper, the use of the Hinge feature is proposed to avoid segmentation and staff-line removal for effective feature extraction from low quality scans. The use of an auto encoder in Hinge feature space is suggested as an alternative to staff-line removal by image processing, and their performance is compared. The result of the experiment shows an accuracy of 87 % for the dataset containing 84 writers’ samples, and superiority of our segmentation and staff-line removal free approach. Practical analysis on Bach’s autograph manuscript of the Well-Tempered Clavier II (Additional MS. 35021 in the British Library, London) is also presented and the extensive applicability of our approach is demonstrated.
AB - Recent renewed interest in computational writer identification has resulted in an increased number of publications. In relation to historical musicology its application has so far been limited. One of the obstacles seems to be that the clarity of the images from the scans available for computational analysis is often not sufficient. In this paper, the use of the Hinge feature is proposed to avoid segmentation and staff-line removal for effective feature extraction from low quality scans. The use of an auto encoder in Hinge feature space is suggested as an alternative to staff-line removal by image processing, and their performance is compared. The result of the experiment shows an accuracy of 87 % for the dataset containing 84 writers’ samples, and superiority of our segmentation and staff-line removal free approach. Practical analysis on Bach’s autograph manuscript of the Well-Tempered Clavier II (Additional MS. 35021 in the British Library, London) is also presented and the extensive applicability of our approach is demonstrated.
KW - Domain driven data mining
KW - Image processing
KW - Optical music recognition
KW - Writer identification
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U2 - 10.1007/s11042-015-2583-8
DO - 10.1007/s11042-015-2583-8
M3 - Article
AN - SCOPUS:84928791188
SN - 1380-7501
VL - 75
SP - 6463
EP - 6479
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 11
ER -