We present two new data sets for automatic evaluation of
tempo estimation and key detection algorithms. In contrast
to existing collections, both released data sets focus
on electronic dance music (EDM). The data sets have been
automatically created from user feedback and annotations
extracted from web sources. More precisely, we utilize
user corrections submitted to an online forum to report
wrong tempo and key annotations on the Beatport website.
Beatport is a digital record store targeted ...
We present two new data sets for automatic evaluation of
tempo estimation and key detection algorithms. In contrast
to existing collections, both released data sets focus
on electronic dance music (EDM). The data sets have been
automatically created from user feedback and annotations
extracted from web sources. More precisely, we utilize
user corrections submitted to an online forum to report
wrong tempo and key annotations on the Beatport website.
Beatport is a digital record store targeted at DJs and focusing
on EDM genres. For all annotated tracks in the data
sets, samples of at least one-minute-length can be freely
downloaded. For key detection, further ground truth is extracted
from expert annotations manually assigned to Beatport
tracks for benchmarking purposes. The set for tempo
estimation comprises 664 tracks and the set for key detection
604 tracks. We detail the creation process of both data
sets and perform extensive benchmarks using state-of-theart
algorithms from both academic research and commercial
products.
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