Convolutional Neural Networks and Ensemble Methods to Identify Musical Elements in Optical Music Recognition

Autores

  • Jenaro Augusto Barbosa Universidade Federal de São João del-Rei
  • Edimilson Batista dos Santos Federal University of São João del-Rei

Palavras-chave:

Optical Music Recognition, Convolutional Neural Network, Ensemble Learning Methods

Resumo

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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Publicado

2022-12-30

Como Citar

Augusto Barbosa, J., & Batista dos Santos, E. (2022). Convolutional Neural Networks and Ensemble Methods to Identify Musical Elements in Optical Music Recognition. Revista Eletrônica De Iniciação Científica Em Computação, 20(4). Recuperado de https://sol.sbc.org.br/journals/index.php/reic/article/view/2761