Extraction of Musical Motifs from Handwritten Music Score Images

Benammar Riyadh, Véronique Eglin, Christine Largeron

2019

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 (Constrained 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

Paper Citation

in Harvard Style

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 (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 428-435. DOI: 10.5220/0007577404280435

in Bibtex Style

@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 (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={428-435},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007577404280435},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - 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
PB - SciTePress