Print Email Facebook Twitter The MediaEval 2017 AcousticBrainz Genre Task Title The MediaEval 2017 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources Author Bogdanov, Dmitry (Pompeu Fabra University) Porter, Alastair (Pompeu Fabra University) Urbano, Julián (TU Delft Multimedia Computing) Schreiber, Hendrik (Tagtraum Industries Incorporated) Contributor Gravier, Guillaume (editor) Bischke, Benjamin (editor) Demarty, Claire-Hélène (editor) Zaharieva, Maia (editor) Riegler, Michael (editor) Dellandrea, Emmanuel (editor) Bogdanov, Dmitry (editor) Sutcliffe, Richard (editor) Jones, Gareth J.F. (editor) Larson, Martha (editor) Date 2017 Abstract This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2017 Benchmarking Initiative for Multimedia Evaluation. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems. We present the task challenges, the employed ground-truth information and datasets, and the evaluation methodology. To reference this document use: http://resolver.tudelft.nl/uuid:7fe6c4fe-d375-4b27-816c-4bdf04b26a1f Source Working Notes Proceedings of the MediaEval 2017 Workshop Event MediaEval 2017, 2017-09-13 → 2017-09-15, Dublin, Ireland Series CEUR Workshop Proceedings, 1613-0073, 1984 Part of collection Institutional Repository Document type conference paper Rights © 2017 Dmitry Bogdanov, Alastair Porter, Julián Urbano, Hendrik Schreiber Files PDF 36047072.pdf 452.81 KB Close viewer /islandora/object/uuid:7fe6c4fe-d375-4b27-816c-4bdf04b26a1f/datastream/OBJ/view