Convolutional Neural Networks and Ensemble Methods to Identify Musical Elements in Optical Music Recognition
Palavras-chave:Optical Music Recognition, Convolutional Neural Network, Ensemble Learning Methods
Optical Music Recognition (OMR) is an important tool to recognize a scanned page of music sheet automatically, which has been applied to preserving music scores. In this paper, we present a comparative study among a Convolutional Neural Network (CNN) architecture, named CREATES, and Ensemble Learning methods, such as Random Forest and XGBoost, to classify musical symbols. The initial results show that CREATES is promising in this task and outperforms ensemble methods on the HOMUS dataset. However, CNN require more computing power.
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