An Online Tool for Semi-Automatically Annotating Music Scores for Optical Music Recognition

被引：0

作者：

Graczyk, Stanislaw ^{[1
]}

Piniarska, Zuzanna ^{[1
]}

Kalamoniak, Mateusz ^{[1
]}

Lukaszewski, Tomasz ^{[1
]}

Lukasik, Ewa ^{[1
]}

机构：

[1] Poznan Univ Tech, Poznan, Poland

来源：

PROCEEDINGS OF 11TH INTERNATIONAL CONFERENCE ON DIGITAL LIBRARIES FOR MUSICOLOGY, DLFM 2024 | 2024年

关键词：

Optical Music Recognition; OMR; musical documents annotation; deep neural networks; ontology; music encoding; MEI;

D O I：

10.1145/3660570.3660571

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The paper describes an online tool, OMRAT, for semi-automatic annotation of music scores for Optical Music Recognition (OMR) systems. OMRAT uses deep neural networks, machine learning, and music notation ontologies at different stages to respectively detect musical objects, establish relationships between them, and convert them into a machine-readable format MEI. A human editor verifies the output of the recognition stage to correct potential errors and remove incorrect labels as needed. The tool can create training/testing datasets for OMR systems and may be used for notation editors or audio synthesizers.

引用

页码：73 / 77

页数：5

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