@article{article_823266, title={Recognizing Musical Notation Using Convolutional Neural Networks}, journal={Avrupa Bilim ve Teknoloji Dergisi}, pages={283–290}, year={2020}, DOI={10.31590/ejosat.823266}, author={Othman, Ahmad and Direkoğlu, Cem}, keywords={Optik müzik tanıma, evrişimli sinir ağları, destek vektör makinesi, nota tanıma}, abstract={Musical scores are the essential of music theory and its development. Musical notation was developed by Greeks around 521 BCE, considering that music was developed a long time ago will will find a gap between new musical technology and old scrpits of music theory since they were written in. However, having music scores in written form has rised various kinds of problems for music information retrieval (MIR). Music notation recognition is a type of optical character recognition (OCR) applications, which allow us to recognize musical scores and convert it to a format that can be editied or played on computer such as musicXML (for page layout). In this paper, we introduce a Convolutional Neural Networks (CNN) based framework for musical notation recognition in images. We use a popular pre-trained CNN network, namely ResNet-101 to extract global features of notation and rest images. Then, a Support Vector Machine (SVM) is employed for training and classification purpose. ResNet-101 is one of the state-of-art pre-trained network for image recognition, ResNet-101 trained with more than a million images. Multiclass SVM classifiers using a fast-linear solver is also very powerful classifier. We also evaluated the proposed approach on a dataset that was derived from Attwenger, P RecordLabel and OMR-dataset, and then labeled manually by music theory. As a result, we can separate notes and rests from each other with an average accuracy of 99.02%. We can also classify five different note types. This is the first time that Resnet-101 and a SVM is combined together to perform musical notation recognition, and results are very promising.}, publisher={Osman SAĞDIÇ}