@InProceedings{ArnauBaro2022,
author="Arnau Baro
and Pau Riba
and Alicia Fornes",
title="Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network",
booktitle="Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022)",
year="2022",
volume="13639",
pages="171--184",
optkeywords="Object detection",
optkeywords="Optical music recognition",
optkeywords="Graph neural network",
abstract="During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note{\textquoteright}s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results.",
optnote="DAG; 600.162; 600.140; 602.230",
optnote="exported from refbase (http://158.109.8.37/show.php?record=3740), last updated on Thu, 20 Apr 2023 11:51:06 +0200",
doi="10.1007/978-3-031-21648-0_12"
}