TY  - CONF
AU  - Arnau Baro
AU  - Pau Riba
AU  - Alicia Fornes
A2  - ICFHR
PY  - 2022//
TI  - Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network
T2  - LNCS
BT  - Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022)
SP  - 171
EP  - 184
VL  - 13639
KW  - Object detection
KW  - Optical music recognition
KW  - Graph neural network
N2  - 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’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.
UR  - http://dx.doi.org/10.1007/978-3-031-21648-0_12
N1  - DAG; 600.162; 600.140; 602.230
ID  - Arnau Baro2022
ER  -