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.
Score following has been an important area of research in AI and music since the mid 80's. Various systems were developed, but they were predominantly for providing automated accompaniment to live concert performances, dealing mostly with issues relating to pitch detection and identification of embellished melodies. They have a big potential in the area of education where student performers benefit in practice situations. Current accompaniment systems are not designed to deal with errors that may occur during practising. In this paper we present a system developed to provide accompaniment for students practising at home. First a survey of score following will be given. Then the capabilities of the system will be explained, and the results from the first experiments of the monophonic score following system will be presented.
Historical musicologists have been seeking for objective and powerful techniques to collect, analyse and verify their findings for many decades. The aim of this study was to show the importance of such domain-specific problems to achieve actionable knowledge discovery in the real the world. Our focus is on finding evidence for the chronological ordering of J.S. Bach's manuscripts, by proposing a musicologist-driven mining method for extracting quantitative information from early music manuscripts. Bach's Cclefs were extracted from a wide range of manuscripts under the direction of domain experts, and with these the classification of C-clefs was conducted. The proposed methods were evaluated on a dataset containing over 1000 clefs extracted from J.S. Bach's manuscripts. The results show more than 70% accuracy for dating J.S. Bach's manuscripts. Dating of Bach's lost manuscripts was quantitatively hypothesized, providing a rough barometer to be combined with other evidence to evaluate musicologists' hypotheses, and the practicability of this domain-driven approach is demonstrated.
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