Abstract
This paper presents the MusiClef data set, a multimodal data set
of professionally annotated music. It includes editorial meta-data
about songs, albums, and artists, as well as MusicBrainz identifiers
to facilitate linking to other data sets. In addition, several audio
features (generic low-level descriptors and state-of-the-art music
features) are provided. Different sets of annotations as well as
music context data – collaboratively generated user tags, web pages
about artists and albums, and the annotation labels provided by music
experts – are included too. Versions of this data set were used
in the MusiCLEF 2011 and in the MusiClef 2012 evaluation
campaigns for auto-tagging tasks.
In this paper, we report on the motivation for the data set, on
its composition, on related sets, and on the evaluation campaigns
in which versions of the set were already used. These campaigns
likewise represent one use case, i.e. music auto-tagging, of the data
set. The complete data set is publicly available for download at
http://www.cp.jku.at/musiclef.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 4th ACM Multimedia Systems Conference (MMSys 2013) |
| Number of pages | 6 |
| Publication status | Published - Mar 2013 |
Fields of science
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
JKU Focus areas
- Computation in Informatics and Mathematics
- Engineering and Natural Sciences (in general)