The MediaEval 2017 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 - Multimedia Computing)
Hendrik Schreiber (Tagtraum Industries Incorporated)
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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.