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Researchers propose a methodology for creating a standard testbed for Optical Music Recognition (OMR), addressing the lack of evaluation standards and benchmark datasets. They collect human preference data to serve as a gold standard for comparing MusicXML automated evaluation metrics and provide an OMR benchmark dataset prototype with ground truth at the symbol and end-to-end levels. The main contributions include a corpus-based "evaluating evaluation" methodology, a corpus of 1230 human preference judgments, definitions of ground truths for Common Western Music Notation scores, and a prototype benchmark called MUSCIMA++ with multiple levels of ground truth.

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Researchers propose a methodology for creating a standard testbed for Optical Music Recognition (OMR), addressing the lack of evaluation standards and...

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  • File Format: PDF
  • File Size: 810 KB
  • Pages: 7
  • Language: EN
  • Total Downloads: 276
  • Last Updated: 2 hours ago

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