Authors:
Benammar Riyadh
1
;
Véronique Eglin
1
and
Christine Largeron
2
Affiliations:
1
Université De Lyon CNRS INSA-Lyon, LIRIS, UMR5205, F-69621 and France
;
2
UJM-Saint-Etienne, CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Etienne and France
Keyword(s):
Musical Motifs Extraction, Transcription, Handwritten Music Scores Analysis.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Shape Representation and Matching
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 (Co
nstrained 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
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