2017年04月01日

OMR(光学楽譜認識)の研究開発のための,アノテーション情報つきデータセット「MUSCIMA++」.

Web拍手:


「In Search of a Dataset for Handwritten Optical Music Recognition: Introducing MUSCIMA++」
https://arxiv.org/pdf/1703.04824.pdf

「MUSCIMA++ | ÚFAL」
https://ufal.mff.cuni.cz/muscima


MUSCIMA++ is a dataset of handwritten music notation for musical symbol detection. It contains 91255 symbols, consisting of both notation primitives and higher-level notation objects, such as key signatures or time signatures. There are 23352 notes in the dataset, of which 21356 have a full notehead, 1648 have an empty notehead, and 348 are grace notes. Composite objects, such as notes, are captured through explicitly annotated relationships of the notation primitives (noteheads, stems, beams...). This way, the annotation provides an explicit bridge between the low-level and high-level symbols described in Optical Music Recognition literature.


MUSCIMA++ has annotations for 140 images from the CVC-MUSCIMA dataset [2], used for handwritten music notation writer identification and staff removal. CVC-MUSCIMA consists of 1000 binary images: 20 pages of music were each re-written by 50 musicians, binarized, and staves were removed. We had 7 different annotators marking musical symbols: each annotator marked one of each 20 CVC-MUSCIMA pages, with the writers selected so that the 140 images cover 2-3 images from each of the 50 CVC-MUSCIMA writers. This setup ensures maximal variability of handwriting, given the limitations in annotation resources.


「The CVC-MUSCIMA Database」
http://www.cvc.uab.es/cvcmuscima/index_database.html

「4. Annotation Instructions − MUSCIMarker 1.0a documentation」
https://muscimarker.readthedocs.io/en/develop/instructions.html

「Tools for working with the MUSCIMA++ dataset of handwritten music notation.」
https://github.com/hajicj/muscima

「muscima – tools for the MUSCIMA++ dataset − muscima 0.3.0 documentation」
https://muscima.readthedocs.io/






OMR(光学楽譜認識)の研究開発のための,アノテーション情報つきデータセット「MUSCIMA++」.




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