Applying Automatic Translation for Optical Music Recognition’s Encoding Step

• Published in: Applied sciences Vol. 11; no. 9; p. 3890
• Main Authors: Ríos-Vila, Antonio, Esplà-Gomis, Miquel, Rizo, David, Ponce de León, Pedro J., Iñesta, José M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2021
• Subjects: computer vision; Cultural heritage; Format; humdrum; intermediate representation; Languages; Machine learning; Machine translation; Musical notation; Musicology; optical music recognition; Semantics; Statistical analysis; Translation
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11093890

Optical music recognition is a research field whose efforts have been mainly focused, due to the difficulties involved in its processes, on document and image recognition. However, there is a final step after the recognition phase that has not been properly addressed or discussed, and which is relevant to obtaining a standard digital score from the recognition process: the step of encoding data into a standard file format. In this paper, we address this task by proposing and evaluating the feasibility of using machine translation techniques, using statistical approaches and neural systems, to automatically convert the results of graphical encoding recognition into a standard semantic format, which can be exported as a digital score. We also discuss the implications, challenges and details to be taken into account when applying machine translation techniques to music languages, which are very different from natural human languages. This needs to be addressed prior to performing experiments and has not been reported in previous works. We also describe and detail experimental results, and conclude that applying machine translation techniques is a suitable solution for this task, as they have proven to obtain robust results.