TY - GEN
T1 - Content-Based Image Classification for Sheet Music Books Recognition
AU - Lozano-Mejia, Diego Jesus
AU - Vega-Uribe, Enrique Paul
AU - Ugarte, Willy
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/21
Y1 - 2020/10/21
N2 - Modern digital music libraries have grown to contain a very large number of musical representation and retrieving images from them may be difficult for people with no prior experience. This study presents a comparison of several convolutional neural networks (CNN) architectures performance on music sheet classification, which are state-of-The-Art computer vision methods to perform classification tasks. The models were trained using randomly selected sheets from different sheet music books and used to classify the source book of the validation data. To evaluate the models with incomplete images, we divide each image of our dataset in nine equal parts, then test the models with them. Performance evaluation of the CNNs prove that they can be very effective in this task.
AB - Modern digital music libraries have grown to contain a very large number of musical representation and retrieving images from them may be difficult for people with no prior experience. This study presents a comparison of several convolutional neural networks (CNN) architectures performance on music sheet classification, which are state-of-The-Art computer vision methods to perform classification tasks. The models were trained using randomly selected sheets from different sheet music books and used to classify the source book of the validation data. To evaluate the models with incomplete images, we divide each image of our dataset in nine equal parts, then test the models with them. Performance evaluation of the CNNs prove that they can be very effective in this task.
KW - CNN
KW - Deep Learning
KW - Sheet Music
UR - https://www.scopus.com/pages/publications/85097820214
U2 - 10.1109/EIRCON51178.2020.9254010
DO - 10.1109/EIRCON51178.2020.9254010
M3 - Contribución a la conferencia
AN - SCOPUS:85097820214
T3 - Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020
BT - Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Engineering International Research Conference, EIRCON 2020
Y2 - 21 October 2020 through 23 October 2020
ER -