New paper accepted: Antonio-Javier Gallego, Pablo Gil, Antonio Pertusa. “Segmentation of Oil Spills on Side-Looking Airborne Radar imagery with Autoencoders”. In: Sensors.
New book published: “Machine Learning and Music Generation”. José M. Iñesta, Darrell Conklin, Rafael Ramirez & Thomas M. Fiore. Ed. Routledge – Taylor & Francis.
New paper accepted: Antonio Pertusa, Antonio-Javier Gallego, Marisa Bernabeu: “MirBot, a collaborative object recognition system for smartphones using convolutional neural networks”. In: Neurocomputing.
New paper published: David Rizo, Beatriz Pascual, José M. Iñesta, Luis Antonio González, Antonio Ezquerro. “TOWARDS THE DIGITAL ENCODING OF HISPANIC WHITE MENSURAL NOTATION”. In: Anuario Musical, Nº 72 enero-diciembre 2017
New paper accepted: Antonio-Javier Gallego, Damián López, and Jorge Calera-Rubio. “Grammatical inference of directed acyclic graph languages with polynomial time complexity”. In: Journal of Computer and System Sciences
New paper accepted: David Rizo, Nieves Pascual, Craig Sapp. “White mensural manual encoding: from **kern to MEI”. In: Music Encoding Conference 2018
New communication presented: Javier Sober Mira, David Rizo, Jorge Calvo-Zaragoza, José M. Iñesta. “Pen-based Music Document Transcription” was presented at the 12th International Workshop on Graphics Recognition GREC 2017.
New communication presented: Jorge Calvo, J.Javier Valero-Mas, Antonio Pertusa. “End-To-End Optical Music Recognition using Neural Networks” was presented at the Intenational Society for Music Information Retrieval Conference (ISMIR), Suzhou, China, 2017
New communication presented: María Hontanilla, Carlos Pérez-Sancho, José M. Iñesta. “Music style recognition with language models – beyond statistical results” was presented at the 10th International Workshop on Machine Learning and Music, Barcelona, 2017
New paper accepted: Antonio-Javier Gallego, Jorge Calvo-Zaragoza, Jose J. Valero-Mas, and Juan Ramón Rico-Juan. “Clustering-based k-Nearest Neighbor Classification for Large-Scale Data with Neural Codes Representation”. In: Pattern Recognition