Recognition of music symbol notation using convolutional neural network

Ciara Setyo, Gede Putra Kusuma


Musical notation is one thing that needs to be learned to play music. This notation has an important role in music because it can help in visualizing instructions for playing musical instruments and singing. Unfortunately, musical symbols that are commonly written in musical notation are difficult for beginners who have just started learning music. This research proposed a solution to create an optical music recognition (OMR) using a deep learning model to classify musical notes more accurately with some of the latest convolutional neural network (CNN) architectures. The research was carried out by implementing vision transformer (ViT), CoAtNet-0, and ConvNeXt-Tiny architecture. The training process was also combined with data augmentation to provide more information for the model to learn. Then the accuracy results of each model were compared to find out the best model for the OMR solution in this research. This experiment uses the Andrea dataset and Attwenger dataset which both get the best result by using the augmentation method and ConvNeXt-Tiny as the model. The best accuracy for the Andrea dataset is 98.15% and for the Attwenger dataset is 98.43%.


Convolutional neural network; Data augmentation; Deep learning; Musical notes; Optical music recognition

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).