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Staff-line removal with selectional auto-encoders
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Información del item - Informació de l'item - Item information Título: Staff-line removal with selectional auto-encoders Autor/es: | Calvo-Zaragoza, Jorge Grupo/s de investigación o GITE: Reconocimiento de Formas e Inteligencia Artificial Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos Palabras clave: Staff-line removal | Optical music recognition | Auto-encoders | Convolutional networks Área/s de conocimiento: Lenguajes y Sistemas Informáticos Fecha de publicación: 15-dic-2017 Editor: Elsevier Cita bibliográfica: Expert Systems with Applications. 2017, 89: 138-148. doi:10.1016/j.eswa.2017.07.002 Resumen: 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. Patrocinador/es: This work was partially supported by the Spanish Ministerio de Educación, Cultura y Deporte through a FPU fellowship (AP2012- 0939) and the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R, supported by UE FEDER funds). URI: http://hdl.handle.net/10045/68971 ISSN: 0957-4174 (Print) | 1873-6793 (Online) DOI: 10.1016/j.eswa.2017.07.002 Idioma: eng Tipo: info:eu-repo/semantics/article Derechos: © 2017 Elsevier Ltd. Revisión científica: si Versión del editor: http://dx.doi.org/10.1016/j.eswa.2017.07.002 Aparece en las colecciones: INV - GRFIA - Artículos de Revistas
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