White Mensural Manual Encoding: from Humdrum to MEI

Most Optical Music Recognition workflows include several steps to retrieve the content from music score images. These steps typically comprise preprocessing, recognition, notation reconstruction and encoding. Currently, state-of-the-art models allow performing graphic recognition in an almost end-to-end fashion, performing the steps from preprocessing to recognition simultaneously. However, this graphic recognition has to be further processed to obtain a standard digital music representation. In this paper, we study the simultaneous recognition and encoding for a state-of-the-art OMR approach, based on neural networks, which receives a single staff-region image as input and directly obtains a sequence of characters that encodes the content in a standard music format. Our results confirm that performing OMR this way is feasible and brings additional benefits such as directly obtaining a version of the score readily available to be further processed or edited by standard tools. CCS CONCEPTS • Computing methodologies → Neural networks.

show abstract

Applying Automatic Translation for Optical Music Recognition’s Encoding Step

Ríos-Vila

Esplà-Gomis

Rizo

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

White Mensural Manual Encoding: from Humdrum to MEI

Cited by 3 publications

References 6 publications

The Mensural Scoring-up Tool

The Mensural Scoring-up Tool

Evaluating Simultaneous Recognition and Encoding for Optical Music Recognition

Applying Automatic Translation for Optical Music Recognition’s Encoding Step

Contact Info

Product

Resources

About