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      Big Data Optical Music Recognition with Multi Images and Multi Recognisers

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      Electronic Visualisation and the Arts (EVA 2014) (EVA)

      Electronic Visualisation and the Arts (EVA 2014)

      8 - 10 July 2014

      Optical music recognition, Big data, Music corpus analysis, Music information retrieval

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          Abstract

          In this paper we describe work in progress towards Multi-OMR, an approach to Optical Music Recognition (OMR) which aims to significantly improve the accuracy of musical score digitisation. There are a large number of scores available in public databases, as well as a range of different commercial and open source OMR tools. Using these resources, we are exploring a Big Data approach to harnessing datasets by aligning and combining the results of multiple versions of the same score, processed with multiple technologies. It is anticipated that this approach will yield high quality results, opening up large datasets to researchers in the field of digital musicology.

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          Author and article information

          Affiliations
          ICSRiM - University of Leeds,

          School of Computing and School of

          Music, Leeds LS2 9JT, UK
          ICSRiM - University of Leeds,

          School of Music,

          Leeds LS2 9JT, UK
          Lancaster Institute for the

          Contemporary Arts,

          Lancaster University, LA1 4YW, UK
          Contributors
          Conference
          July 2014
          July 2014
          : 215-218
          10.14236/ewic/EVA2014.50
          © Kia Ng et al. Published by BCS Learning and Development Ltd. Electronic Visualisation and the Arts (EVA 2014), London, UK

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Electronic Visualisation and the Arts (EVA 2014)
          EVA
          London, UK
          8 - 10 July 2014
          Electronic Workshops in Computing (eWiC)
          Electronic Visualisation and the Arts (EVA 2014)
          Product
          Product Information: 1477-9358 BCS Learning & Development
          Self URI (journal page): https://ewic.bcs.org/
          Categories
          Electronic Workshops in Computing

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