| Records |
| Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
| Title |
Optical Music Recognition by Recurrent Neural Networks |
Type |
Conference Article |
| Year |
2017 |
Publication |
14th IAPR International Workshop on Graphics Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
25-26 |
| Keywords |
Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory |
| Abstract |
Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level |
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ICDAR |
| Notes |
DAG; 600.097; 601.302; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BRC2017 |
Serial |
3056 |
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| Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
| Title |
Optical Music Recognition by Long Short-Term Memory Networks |
Type |
Book Chapter |
| Year |
2018 |
Publication |
Graphics Recognition. Current Trends and Evolutions |
Abbreviated Journal |
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| Volume |
11009 |
Issue |
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Pages |
81-95 |
| Keywords |
Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory |
| Abstract |
Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. |
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| Publisher |
Springer |
Place of Publication |
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Editor |
A. Fornes, B. Lamiroy |
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LNCS |
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Edition |
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ISBN |
978-3-030-02283-9 |
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Conference |
GREC |
| Notes |
DAG; 600.097; 601.302; 601.330; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BRC2018 |
Serial |
3227 |
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| Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
| Title |
From Optical Music Recognition to Handwritten Music Recognition: a Baseline |
Type |
Journal Article |
| Year |
2019 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
123 |
Issue |
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Pages |
1-8 |
| Keywords |
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| Abstract |
Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community. |
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| Notes |
DAG; 600.097; 601.302; 601.330; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BRC2019 |
Serial |
3275 |
| Permanent link to this record |
