End-to-end optical music recognition for pianoform sheet music

被引:0
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作者
Antonio Ríos-Vila
David Rizo
José M. Iñesta
Jorge Calvo-Zaragoza
机构
[1] University of Alicante,U.I for Computing Research
[2] Instituto Superior de Enseñanzas Artísticas de la Comunidad Valenciana (ISEA. CV),undefined
关键词
Optical music recognition; Polyphonic music scores; GrandStaff; Neural networks;
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摘要
End-to-end solutions have brought about significant advances in the field of Optical Music Recognition. These approaches directly provide the symbolic representation of a given image of a musical score. Despite this, several documents, such as pianoform musical scores, cannot yet benefit from these solutions since their structural complexity does not allow their effective transcription. This paper presents a neural method whose objective is to transcribe these musical scores in an end-to-end fashion. We also introduce the GrandStaff dataset, which contains 53,882 single-system piano scores in common western modern notation. The sources are encoded in both a standard digital music representation and its adaptation for current transcription technologies. The method proposed in this paper is trained and evaluated using this dataset. The results show that the approach presented is, for the first time, able to effectively transcribe pianoform notation in an end-to-end manner.
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页码:347 / 362
页数:15
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