MusiClef 2012
Multimodal Music Data Set
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MusiClef 2012 Multimodal Music Data Set
This page contains the
MusiClef 2012 data set, a paper on which was accepted for ACM MMSys 2013
(Dataset
Session).
The data set contains multimodal data on 1355 popular music songs by
218 leading artists, and is a considerably expanded version of the data
set that was used for the MusiClef
Multimodal Music Tagging Task at MediaEval 2012. Following the
corpus annotation standard recently proposed by Peeters and Fort at
ISMIR 2012 (reference [10] in
our paper), the data set
used at MediaEval 2012 would be identified as corpus:MIR:MusiClef:2012:MediaEval:version1.0,
while the data set on this page would be identified as corpus:MIR:MusiClef:2012:MMSys:version1.0.
In case you make use of the data set in
your own research, please cite the corresponding paper:
A Professionally Annotated and Enriched
Multimodal Data Set on Popular Music
Schedl, M., Liem, C.C.S., Peeters, G., and Orio, N.
Proceedings of the 4th ACM Multimedia Systems Conference (MMSys 2013),
Oslo, Norway, February-March 2013.
>> PDF, BibTeX
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You can either download the entire
dataset (musiclef_2012_dataset.zip
- 12.5 GB) or
each component separately, below.
Editorial Metadata
To identify the music items in the
data set and link them to other data
sources, we provide lists of artist and songs, the corresponding album
information for the songs, as well as corresponding
MusicBrainz
identifiers. For artists, we further provide shortened representations
("webartist"), which are used to identify artists in the web crawling
subsets (to avoid file naming problems). The file names and
corresponding composition of entries is as follows:
songs.csv |
<song-id, song, artist-id,
artist> |
artists.csv |
<artist-id, artist,
webartist> |
mbids.csv |
<song-id, song-mbid, song,
artist-id, artist-mbid, artist> |
artists-songs-albums-tags.csv
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<song,
artist, album, tag1, tag2, ..., tagN>
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Audio Features
The data set contains different music
content features, computed from the audio signal of the music pieces.
Two low-level features (FB-Mel and MFCCs) are provided, along with the
output of two state-of-the-art audio feature extraction algorithms (BLF
and PS09).
FB-Mel
MFCC
BLF and PS09
Block-Level Features
are a combination of various descriptors that model temporal aspects of
the audio signal. We provide similarity scores between all music pieces
in the collection:
blf_similarities.txt
We further provide the feature vector representations on which these
similarities are computed:
blf_features.txt.
For details about the feature vector composition, please see the
corresponding paper (reference [
17]
in our paper).
PS09
Features are an aggregation of various rhythm features (Fluctuation
Patterns, Onset Patterns, Onset Coefficients) and timbre features
(MFCCs, Spectral Contrast Coecients, Harmonicness, Attackness). Again,
we provide pairwise similarity scores between all music items in the
collection:
ps09_similarities.txt
We further provide the feature vector representations on which these
similarities are computed:
ps09_features.txt.
For details about the feature vector composition, please see the
corresponding paper (reference [
11]
in our paper).
The corresponding assignment between song-ids and feature vectors for
both, BLF and PS09 features can be found in
song_ids.txt.
User Tags
Collaborative tags representing the
"wisdom of the crowds" are provided on the track level. They were
gathered from
Last.fm
and are presented along with corresponding weights, normalized to
the range [0,100]:
lastfm_tags.zip
Web
Pages
To offer contextual music data, we
crawled various sets of web pages in six different languages (English,
German, French, Italian, Spanish, Swedish), on the level of artists and
of releases. The corresponding data sets contain the raw HTML pages,
information about the crawling process, standard vector space
representations of the artists/releases as TF-IDF weight vectors, and
Lucene indices of
the web page sets (artist-level: 6 languages, release-level: English).
The sets are decomposed as follows:
Expert Labels
The music items were tagged by
professional music annotators with respect to genre and mood aspects.
We provide a unique list of tags used by the annotators (
tag_list.csv),
the
<song-id, tag>
assignments which were provided for training in the
MusiClef 2012 campaign (
train.csv),
the test set containing only the song identifiers for which the tags
had to be predicted (
test.csv),
and the ground truth for the test set, i.e., expert tags for the song
identifiers in the test set (
test_with_groundtruth.csv).
MediaEval 2012
Reference Implementation
We provide a reference implementation, in which several text- and
audio-features are fused to create a simple, baseline auto-tagger. The
reference implementation is available as Matlab scripts: reference-implementation.zip.
Please see README.TXT in the root of the zip file for further details.
last
edited by ms
and cl at
2013-02-08