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Authors: Benammar Riyadh 1 ; Véronique Eglin 1 and Christine Largeron 2

Affiliations: 1 Université De Lyon CNRS INSA-Lyon, LIRIS, UMR5205, F-69621, France ; 2 UJM-Saint-Etienne, CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Etienne, France

ISBN: 978-989-758-354-4

Keyword(s): Musical Motifs Extraction, Transcription, Handwritten Music Scores Analysis.

Abstract: A musical motif represents a sequence of musical notes that can determine the identity of a composer or a music style. Musical motifs extraction is of great interest to musicologists to make critical studies of music scores. Musical motifs extraction can be solved by using a string mining algorithm when music data is represented as a sequence. When music data is initially produced in XML or MIDI format or can be converted into those standards, it can be automatically represented as a sequence of notes. So, in this work, starting from digitized images of music scores, our objective is twofold: first, we design a system able to generate musical sequences from handwritten music scores. To address this issue, one of the segmentation-free R-CNN models trained on musical data have been used to detect and recognize musical primitives that are next transcribed into XML sequences. Then, the sequences are processed by a computational model of musical motifs extraction algorithm called CSMA (Con strained String Mining Algorithm). The consistency and performances of the framework are then discussed according to the efficiency of the R-CNN ( Region-proposal Convolutional Neural Network) based recognition system through the estimation of misclassified primitives relating to the detailed account of detected motifs. The carried-out experiments of our complete pipeline show that it is consistent to find more than 70% of motifs with less than 20% of average detection/classification R-CNN errors (More)

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Paper citation in several formats:
Riyadh, B.; Eglin, V. and Largeron, C. (2019). Extraction of Musical Motifs from Handwritten Music Score Images.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-354-4, pages 428-435. DOI: 10.5220/0007577404280435

@conference{visapp19,
author={Benammar Riyadh. and Véronique Eglin. and Christine Largeron.},
title={Extraction of Musical Motifs from Handwritten Music Score Images},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2019},
pages={428-435},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007577404280435},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Extraction of Musical Motifs from Handwritten Music Score Images
SN - 978-989-758-354-4
AU - Riyadh, B.
AU - Eglin, V.
AU - Largeron, C.
PY - 2019
SP - 428
EP - 435
DO - 10.5220/0007577404280435

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