@Inbook{PauRiba2017,
author="Pau Riba
and Alicia Fornes
and Josep Llados",
editor="Bart Lamiroy
and R Dueire Lins",
chapter="Towards the Alignment of Handwritten Music Scores",
title="International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges",
year="2017",
volume="9657",
pages="103--116",
optkeywords="Optical Music Recognition",
optkeywords="Handwritten Music Scores",
optkeywords="Dynamic Time Warping alignment",
abstract="It is very common to nd dierent versions of the same music work in archives of Opera Theaters. These dierences correspond to modications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such dierences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.",
optnote="DAG; 600.097; 602.006; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2955), last updated on Mon, 07 Dec 2020 14:29:24 +0100",
isbn="978-3-319-52158-9",
opturl="https://link.springer.com/chapter/10.1007/978-3-319-52159-6_8",
file=":http://refbase.cvc.uab.es/files/RFL2017.pdf:PDF"
}