Staff-line detection and removal using a convolutional neural network

Calvo-Zaragoza, Jorge; Pertusa, Antonio; Oncina, José

doi:10.1007/s00138-017-0844-4

Cited by 19 publications

(9 citation statements)

References 27 publications

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“…Montagner et al [26] followed a similar approach but learning the parameters of the morphological image operators. Calvo-Zaragoza et al [27] used Support Vector Machines to classify pixels belonging to the categories staff or non-staff, which was later extended by incorporating the use of CNN [28] and Auto-Encoders [29].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Document Processing of Music Score Images

et al. 2018

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There is an increasing interest in the automatic digitization of medieval music documents. Despite efforts in this field, the detection of the different layers of information on these documents still poses difficulties. The use of Deep Neural Networks techniques has reported outstanding results in many areas related to computer vision. Consequently, in this paper, we study the so-called Convolutional Neural Networks (CNN) for performing the automatic document processing of music score images. This process is focused on layering the image into its constituent parts (namely, background, staff lines, music notes, and text) by training a classifier with examples of these parts. A comprehensive experimentation in terms of the configuration of the networks was carried out, which illustrates interesting results as regards to both the efficiency and effectiveness of these models. In addition, a cross-manuscript adaptation experiment was presented in which the networks are evaluated on a different manuscript from the one they were trained. The results suggest that the CNN is capable of adapting its knowledge, and so starting from a pre-trained CNN reduces (or eliminates) the need for new labeled data.

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Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Document Processing of Music Score Images

et al. 2018

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“…The comparison with Pixel method is also illustrative of the goodness of our proposal, since it demonstrates that the performance is not only achieved by using a supervised learning scheme (Pixel also does so) but because of the adequacy of the proposed model. [21] 83.01 2013 NUASI lin [3] 94.29 2008 NUASI skel [3] 93.34 2008 LRDE [11] 97.14 2014 INESC [10] 91.01 2009 NUS [9] 75.24 2012 Pixel [22] 95.04 2016 Image Operator [12] 97.96 2017 StaffNet [26] 97.87 2017 Baseline 97.31 -Our approach 99.32 - We also test the method using grayscale version of the scores. Our approach can easily be used to deal with grayscale images without any additional pre-processing steps like binarization.…”

Section: E Comparison With State-of-the-artmentioning

confidence: 99%

Staff line Removal using Generative Adversarial Networks

Konwer

Bhunia

Bhowmick

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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Staff line removal is a crucial pre-processing step in Optical Music Recognition. It is a challenging task to simultaneously reduce the noise and also retain the quality of music symbol context in ancient degraded music score images. In this paper we propose a novel approach for staff line removal, based on Generative Adversarial Networks. We convert staff line images into patches and feed them into a U-Net, used as Generator. The Generator intends to produce staff-less images at the output. Then the Discriminator does binary classification and differentiates between the generated fake staff-less image and real ground truth staff less image. For training, we use a Loss function which is a weighted combination of L2 loss and Adversarial loss. L2 loss minimizes the difference between real and fake staff-less image. Adversarial loss helps to retrieve more high quality textures in generated images. Thus our architecture supports solutions which are closer to ground truth and it reflects in our results. For evaluation we consider the ICDAR/GREC 2013 staff removal database. Our method achieves superior performance in comparison to other conventional approaches.

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“…Therefore, much effort has been made to successfully solve this stage [24][25][26]. Recently, results have reached values closer to the optimum over standard benchmarks by using DL [27,28].…”

Section: Introductionmentioning

confidence: 99%

“…References Task [20][21][22][23] Pre-processing of music score images [24][25][26][27][28] Staff-line removal [29][30][31][32][33][34] Symbol classification [35][36][37][38][39] Detection, classification, and interpretation [40][41][42][43] OMR in mensural notation…”

Section: Introductionmentioning

confidence: 99%

End-to-End Neural Optical Music Recognition of Monophonic Scores

Calvo-Zaragoza

Rizo

2018

Applied Sciences

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Optical Music Recognition is a field of research that investigates how to computationally decode music notation from images. Despite the efforts made so far, there are hardly any complete solutions to the problem. In this work, we study the use of neural networks that work in an end-to-end manner. This is achieved by using a neural model that combines the capabilities of convolutional neural networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature of the problem. Thanks to the use of the the so-called Connectionist Temporal Classification loss function, these models can be directly trained from input images accompanied by their corresponding transcripts into music symbol sequences. We also present the Printed Images of Music Staves (PrIMuS) dataset, containing more than 80,000 monodic single-staff real scores in common western notation, that is used to train and evaluate the neural approach. In our experiments, it is demonstrated that this formulation can be carried out successfully. Additionally, we study several considerations about the codification of the output musical sequences, the convergence and scalability of the neural models, as well as the ability of this approach to locate symbols in the input score.

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Staff-line detection and removal using a convolutional neural network

Cited by 19 publications

References 27 publications

Deep Neural Networks for Document Processing of Music Score Images

Deep Neural Networks for Document Processing of Music Score Images

Staff line Removal using Generative Adversarial Networks

End-to-End Neural Optical Music Recognition of Monophonic Scores

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