Dataset Open Access

GiantSteps+ EDM Key Dataset

Ángel Faraldo

Data manager(s)
Ángel Faraldo
Editor(s)
Daniel G. Camhi

The GiantSteps+ EDM Key Dataset includes 600 two-minute sound excerpts from various EDM subgenres, annotated with single-key labels. This dataset focus in problematic Beatport excerpts, so it is biased, but it is interesting to test the robustness of key recognition systems. These 600 tracks have been analysed by Daniel G. Camhi and Ángel Faraldo, providing pitch-class set descriptions, key and modal changes, comments and confidence levels for the individual tracks.

This dataset is a revision of the original GiantSteps Key Dataset, available in Github (<https://github.com/GiantSteps/giantsteps-key-dataset>) and initially described in:

Knees, P., Faraldo, Á., Herrera, P., Vogl, R., Böck, S., Hörschläger, F., Le Goff, M. (2015). Two Datasets 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, 364–370. Málaga, Spain.

The original audio samples belong to online audio snippets from Beatport, an online music store for DJ's and Electronic Dance Music Producers (<http:\\www.beatport.com>). If this dataset were used in further research, we would appreciate the citation of the current DOI (10.5281/zenodo.1101082) and the following doctoral dissertation, where a detailed description of the properties of this dataset can be found:

Ángel Faraldo (2017). Tonality Estimation in Electronic Dance Music: A Computational and Musically Informed Examination. PhD Thesis. Universitat Pompeu Fabra, Barcelona.

Files (857.0 MB)
Name Size
annotations.zip
md5:603183d92916313c97dae51edac7655f
408.6 kB Download
audio.zip
md5:8ec9ade888d5a88ce435d7fda031929b
855.8 MB Download
GiantSteps+.xlsx
md5:79ac74fe9d192514da1ffd686226c77b
130.3 kB Download
original_metadata.zip
md5:54181e0f34c35d9720439750d0b08091
654.3 kB Download
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