The MediaEval 2018 AcousticBrainz Genre Task
Content-based Music Genre Recognition from Multiple Sources
Dmitry Bogdanov (Pompeu Fabra University)
Alastair Porter (Pompeu Fabra University)
Julián Urbano (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Hendrik Schreiber (Tagtraum Industries Incorporated)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2018 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.