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Musicologist-driven writer identification in early music manuscripts

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Abstract

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.

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Notes

  1. It is often the case that libraries do not allow us to return to source.

  2. Javis [9] claims to have conducted his research this way. However, his application was not sufficiently transparent.

  3. See http://www.cs.waikato.ac.nz/ml/weka for more information about Weka. Normalised poly kernel was used as the kernel function of SVM and sequential minimal optimisation was conducted [15]. The number of trees was set to 100 for RF.

  4. Franklin’s classification is considered [7] . This conclusion was not based on handwriting analysis but the studies on paper, rastral and the diplomatic features of the score.

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Authors and Affiliations

  1. School of Creative Arts, Queen’s University of Belfast, Belfast, UK

    Yo Tomita

  2. Department of Artificial Intelligence, University of Groningen, Groningen, Netherlands

    Lambert Schomaker & Jean-Paul van Oosten

  3. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, UK

    David Bell

  4. Department of Media Technology, College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan

    Masahiro Niitsuma

Authors
  1. Masahiro Niitsuma
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  2. Lambert Schomaker
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  3. Jean-Paul van Oosten
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  4. Yo Tomita
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  5. David Bell
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Correspondence to Masahiro Niitsuma.

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Niitsuma, M., Schomaker, L., Oosten, JP.v. et al. Musicologist-driven writer identification in early music manuscripts. Multimed Tools Appl 75, 6463–6479 (2016). https://doi.org/10.1007/s11042-015-2583-8

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  • DOI: https://doi.org/10.1007/s11042-015-2583-8

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