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.
- Willi Apel. 1953. The notation of polyphonic music, 900--1600. (1953), 1--532.Google Scholar
- Jorge Aracil Pérez. 2018. Modelos computacionales para la transcripción musical. Master's thesis. Universidad Internacional de La Rioja.Google Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- Laurent Pugin. 2009. Editing Renaissance Music: The Aruspix Project. Internationales Jahrbuch für Editionswissenschaften (2009), 94--103.Google Scholar
- 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 Scholar
- 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 Scholar
- Eleanor Selfridge-Field. 1997. Beyond MIDI: The handbook of musical codes. MIT Press, Cambridge, Massachusetts, USA. Google ScholarDigital Library
- 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 ScholarDigital Library
- MuRET: a music recognition, encoding, and transcription tool
Recommendations
Using Optical Music Recognition to Encode 17th-Century Music Prints: The Canonic Works of Paolo Agostini (c.1583–1629) as a Test Case
DLfM '20: Proceedings of the 7th International Conference on Digital Libraries for MusicologyThere have been several attempts to improve the retrieval of symbolic music information by Optical Music Recognition (OMR) to increase the “searchability” of digital music libraries of early music prints and to facilitate the collection of data for ...
Understanding Optical Music Recognition
For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant ...
How current optical music recognition systems are becoming useful for digital libraries
DLfM '18: Proceedings of the 5th International Conference on Digital Libraries for MusicologyOptical Music Recognition (OMR) promises to make large collections of sheet music searchable by their musical content. It would open up novel ways of accessing the vast amount of written music that has never been recorded before. For a long time, OMR ...
Comments