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MuRET: a music recognition, encoding, and transcription tool

Published:28 September 2018Publication History

ABSTRACT

The transcription process from early and modern notation manuscripts to a structured digital encoding has been traditionally performed following a fully manual workflow. At most it has received some technological support in particular stages, like optical music recognition (OMR) of the source images, or transcription to modern notation with music edition applications. Currently, there is no mature and stable enough solution for the OMR problem, and the most used music editors do not support early notations, such as the mensural one. In this work, a new tool called MUsic Recognition, Encoding, and Transcription (MuRET) is introduced, which covers all transcription phases, from the manuscript source to the encoded digital content. MuRET is designed as a technology-focused research tool, allowing different processing approaches to be used, and producing both the expected transcribed contents in standard encodings and data for the study of the transcription process itself.

References

  1. Willi Apel. 1953. The notation of polyphonic music, 900--1600. (1953), 1--532.Google ScholarGoogle Scholar
  2. Jorge Aracil Pérez. 2018. Modelos computacionales para la transcripción musical. Master's thesis. Universidad Internacional de La Rioja.Google ScholarGoogle Scholar
  3. Donald Byrd and Jakob Grue Simonsen. 2015. Towards a Standard Testbed for Optical Music Recognition: Definitions, Metrics, and Page Images. Journal of New Music Research 44, 3 (2015), 169--195.Google ScholarGoogle ScholarCross RefCross Ref
  4. Jorge Calvo-Zaragoza and David Rizo. 2018. Camera-PrIMuS: Neural End-to-End Optical Music Recognition on Realistic Monophonic Scores. In 19th International Society for Music Information Retrieval Conference. (in press).Google ScholarGoogle Scholar
  5. Jorge Calvo-Zaragoza and David Rizo. 2018. End-to-End Neural Optical Music Recognition of Monophonic Scores. Applied Sciences 8, 4 (2018), 606--629.Google ScholarGoogle ScholarCross RefCross Ref
  6. Jorge Calvo-Zaragoza, David Rizo, and José Manuel Iñesta. 2016. Two (Note) Heads Are Better Than One: Pen-Based Multimodal Interaction with Music Scores. In 17th International Society for Music Information Retrieval Conference. 509--514.Google ScholarGoogle Scholar
  7. Jorge Calvo-Zaragoza, Alejandro Héctor Toselli, and Enrique Vidal. 2017. Handwritten Music Recognition for Mensural Notation: Formulation, Data and Baseline Results. In 14th IAPR International Conference on Document Analysis and Recognition. 1081--1086.Google ScholarGoogle Scholar
  8. Vicente Bosch Campos, Jorge Calvo-Zaragoza, Alejandro Héctor Toselli, and Enrique Vidal-Ruiz. 2016. Sheet Music Statistical Layout Analysis. In 15th International Conference on Frontiers in Handwriting Recognition. 313--318.Google ScholarGoogle Scholar
  9. Liang Chen and Christopher Raphael. 2016. Human-Directed Optical Music Recognition. In Document Recognition and Retrieval XXIII, San Francisco, California, USA, February 14--18, 2016. 1--9.Google ScholarGoogle Scholar
  10. Reinier de Valk, Marnix van Berchum, and Peter van Kranenburg. 2017. Music encoding, formats, and data sustainability. Paper presented at the Music Encoding Congerence, Tours (France).Google ScholarGoogle Scholar
  11. Reinier de Valk, Anja Volk, Andre Holzapfel, Aggelos Pikrakis, Nadine Kroher, and Joren Six. 2017. MIRchiving: Challenges and Opportunities of Connecting MIR Research and Digital Music Archives. In Proceedings of the 4th International Workshop on Digital Libraries for Musicology (DLfM '17). ACM, New York, NY, USA, 25--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 580--587. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Alexander Pacha and Jorge Calvo-Zaragoza. 2018. Optical Music Recognition in Mensural Notation with Region-based Convolutional Neural Networks. In 19th International Society for Music Information Retrieval Conference. (in press).Google ScholarGoogle Scholar
  14. Alexander Pacha, Kwon-Young Choi, Bertrand Coüasnon, Yann Ricquebourg, Richard Zanibbi, and Horst Eidenberger. 2018. Handwritten Music Object Detection: Open Issues and Baseline Results. In 13th IAPR Workshop on Document Analysis Systems. 163--168.Google ScholarGoogle Scholar
  15. Laurent Pugin. 2009. Editing Renaissance Music: The Aruspix Project. Internationales Jahrbuch für Editionswissenschaften (2009), 94--103.Google ScholarGoogle Scholar
  16. Laurent Pugin, Rodolfo Zitellini, and Perry Roland. 2014. Verovio - A library for Engraving MEI Music Notation into SVG.. In International Society for Music Information Retrieval.Google ScholarGoogle Scholar
  17. David Rizo, Pascual Beatriz, José Manuel Iñesta, Ezquerro, and Luis Antonio González Marín. 2017. Towards the Digital Encoding of Hispanic White Mensural Notation. Anuario Musical 72 (2017), 293--304.Google ScholarGoogle Scholar
  18. Eleanor Selfridge-Field. 1997. Beyond MIDI: The handbook of musical codes. MIT Press, Cambridge, Massachusetts, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. David M. Weigl, Kevin R. Page, Peter Organisciak, and J. Stephen Downie. 2017. Information-seeking in Large-scale Digital Libraries: Strategies for Scholarly Workset Creation. In Proceedings of the 17th ACM/IEEE Joint Conference on Digital Libraries (JCDL '17). IEEE Press, Piscataway, NJ, USA, 253--256. http://dl.acm.org/citation.cfm?id=3200334.3200365 Google ScholarGoogle ScholarDigital LibraryDigital Library
  1. MuRET: a music recognition, encoding, and transcription tool

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      cover image ACM Other conferences
      DLfM '18: Proceedings of the 5th International Conference on Digital Libraries for Musicology
      September 2018
      101 pages
      ISBN:9781450365222
      DOI:10.1145/3273024

      Copyright © 2018 ACM

      Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 September 2018

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      DLfM '18 Paper Acceptance Rate14of27submissions,52%Overall Acceptance Rate27of48submissions,56%

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