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14th workshop gRFIA

2nd International Workshop on Reading Music Systems


Delft, November 2

12th international workshop on Machine Learning and Music


Würzburg (Germany): September 16, 2019



  1. Calvo-Zaragoza, J.: Pertusa, A.; Oncina, J.
    "Staff-line detection and removal using a convolutional neural network"
    Machine Vision and Applications, pp. 1-10 (2017)
    : bibtex : URL

    Staff-line removal is an important preprocessing stage for most optical music recognition systems. Common procedures to solve this task involve image processing techniques. In contrast to these traditional methods based on hand-engineered transformations, the problem can also be approached as a classification task in which each pixel is labeled as either staff or symbol, so that only those that belong to symbols are kept in the image. In order to perform this classification, we propose the use of convolutional neural networks, which have demonstrated an outstanding performance in image retrieval tasks. The initial features of each pixel consist of a square patch from the input image centered at that pixel. The proposed network is trained by using a dataset which contains pairs of scores with and without the staff lines. Our results in both binary and grayscale images show that the proposed technique is very accurate, outperforming both other classifiers and the state-of-the-art strategies considered. In addition, several advantages of the presented methodology with respect to traditional procedures proposed so far are discussed.

@article {
 author = "Calvo-Zaragoza, J.: Pertusa, A.; Oncina, J.",
 title  = "Staff-line detection and removal using a convolutional neural network",
 issn = "1432-1769",
 journal = "Machine Vision and Applications",
 pages = "1-10",
 year = "2017"
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