Title: MEI2JSON: a pre-processing music scores converter

Authors: Charbel El Achkar; Talar Atéchian

Addresses: TICKETLAB, Antonine University, Baabda, Lebanon ' TICKETLAB, Antonine University, Baabda, Lebanon

Abstract: Converting music score content from symbolic formats to simplified data formats is found useful for artificial intelligence purposes. The conversion can be applied using XSL stylesheets and ontologies to ensure the preserving of the data quality throughout the transformation. In this paper, we proposed a new converter capable of transforming music scores encoded in MEI to JSON format for pre-processing purposes, and future usage into artificial intelligence techniques. The proposed converter uses an eastern music score ontology capable of structuring standard music scores content in addition to elements and attributes specific to eastern music. Thus, the converter shares the same support for eastern music scores. We illustrate the conversion process by assessing the performance analysis, the data quality, and the storage of the proposed converter in comparison with a combined approach composed of two state-of-the-art converters.

Keywords: MEI; MEI2JSON; music scores converter; MusicPatternOWL; eastern music.

DOI: 10.1504/IJIIDS.2022.120130

International Journal of Intelligent Information and Database Systems, 2022 Vol.15 No.1, pp.57 - 77

Received: 16 Nov 2020
Accepted: 27 Apr 2021
Published online: 07 Jan 2022 *