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1st International Workshop on Reading Music Systems


Paris, September 20

PRAIg '18 / +pics


11th international workshop on Machine Learning and Music


Stockholm: 13-15th July 2018



  1. Gallego, Antonio-Javier; Calvo-Zaragoza, Jorge
    "Staff-line removal with Selectional Auto-Encoders"
    Expert Systems With Applications, vol. 89, pp. 138 - 148 (2017)
    : bibtex

    Staff-line removal is an important preprocessing stage as regards most Optical Music Recognition systems. The common procedures employed to carry out this task involve image processing techniques. In contrast to these traditional methods, which are based on hand-engineered transformations, the problem can also be approached from a machine learning point of view if representative examples of the task are provided. We propose doing this through the use of a new approach involving auto-encoders, which select the appropriate features of an input feature set (Selectional Auto-Encoders). Within the context of the problem at hand, the model is trained to select those pixels of a given image that belong to a musical symbol, thus removing the lines of the staves. Our results show that the proposed technique is quite competitive and significantly outperforms the other state-of-art strategies considered, particularly when dealing with grayscale input images.

@article {
 author = "Gallego, Antonio-Javier; Calvo-Zaragoza, Jorge",
 title  = "Staff-line removal with Selectional Auto-Encoders",
 issn = "0957-4174",
 journal = "Expert Systems With Applications",
 pages = "138 - 148",
 volume = "89",
 year = "2017"
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