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Department of
Computational
Perception
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GiantSteps Data Sets
Here you can download the
data sets as described in
P. Knees, Á. Faraldo, P.
Herrera, R. Vogl, S. Böck, F. Hörschläger, and M. Le Goff.
Two Data Sets for Tempo Estimation and Key Detection in Electronic
Dance Music Annotated from User Corrections.
In Proceedings of the 16th
International Society for Music Information Retrieval Conference (ISMIR),
Málaga, Spain, 2015.
(PDF,
BibTeX)
Please cite this paper if you
make use of one of the data sets in your work.
Download:
GiantSteps Tempo Set
The current version of the GiantSteps Tempo data set is hosted on
GitHub (https://github.com/GiantSteps/giantsteps-tempo-dataset)
To clone, install git and
type
git
clone https://github.com/GiantSteps/giantsteps-tempo-dataset.git
The cloned repository includes the ground truth annotations in various
formats, as well as scripts to download all 664 mp3 files and to
convert them to wav format.
Benchmarks
Below are performance measures of different published tempo
detection algorithms, as well as commercial products. If you have
additional results from other published approaches, please get in touch
so we can include them in this list to serve as an up-to-date reference
of the state-of-the-art.
GiantSteps Key Set
The current version of the GiantSteps Key data set is hosted on GitHub (https://github.com/GiantSteps/giantsteps-key-dataset)
To clone, install git and
type
git
clone https://github.com/GiantSteps/giantsteps-key-dataset.git
The cloned repository includes the ground truth annotations in various
formats, as well as scripts to download all 604 mp3 files and to
convert
them to wav format.
Benchmarks
Below are performance measures of different published key
detection algorithms, as well as commercial products. If you have
additional results from other published approaches, please get in touch
so we can include them in this list to serve as an up-to-date reference
of the state-of-the-art.
Raw Data + Source Code for
Extraction
last
edited by pk on Nov 10, 2015