| Records |
| Author |
Arnau Baro; Pau Riba; Alicia Fornes |
| Title |
Towards the recognition of compound music notes in handwritten music scores |
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Conference Article |
| Year |
2016 |
Publication  |
15th international conference on Frontiers in Handwriting Recognition |
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| Abstract |
The recognition of handwritten music scores still remains an open problem. The existing approaches can only deal with very simple handwritten scores mainly because of the variability in the handwriting style and the variability in the composition of groups of music notes (i.e. compound music notes). In this work we focus on this second problem and propose a method based on perceptual grouping for the recognition of compound music notes. Our method has been tested using several handwritten music scores of the CVC-MUSCIMA database and compared with a commercial Optical Music Recognition (OMR) software. Given that our method is learning-free, the obtained results are promising. |
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Shenzhen; China; October 2016 |
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2167-6445 |
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ICFHR |
| Notes |
DAG; 600.097 |
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Admin @ si @ BRF2016 |
Serial |
2903 |
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| Author |
Arnau Baro; Pau Riba; Alicia Fornes |
| Title |
A Starting Point for Handwritten Music Recognition |
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Conference Article |
| Year |
2018 |
Publication |
1st International Workshop on Reading Music Systems |
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Pages |
5-6 |
| Keywords |
Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA |
| Abstract |
In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community. |
| Address |
Paris; France; September 2018 |
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WORMS |
| Notes |
DAG; 600.097; 601.302; 601.330; 600.121 |
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no |
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Admin @ si @ BRF2018 |
Serial |
3223 |
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| Author |
Arnau Baro; Pau Riba; Alicia Fornes |
| Title |
Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network |
Type |
Conference Article |
| Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
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13639 |
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Pages |
171-184 |
| Keywords |
Object detection; Optical music recognition; Graph neural network |
| Abstract |
During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note’s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results. |
| Address |
December 04 – 07, 2022; Hyderabad, India |
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ICFHR |
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DAG; 600.162; 600.140; 602.230 |
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Admin @ si @ BRF2022b |
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3740 |
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