Giantsteps+ Edm Key Dataset

<em>The GiantSteps+ EDM Key Dataset</em> includes 600 two-minute sound excerpts from various EDM subgenres, annotated with single-key labels, comments and confidence levels by Daniel G. Camhi, and thoroughly revised and expanded by Ángel Faraldo. Additionally, 500 tracks have been thoroughly analysed, containing pitch-class set descriptions, key changes, and additional modal changes. This dataset is a revision of the original GiantSteps Key Dataset, available in Github (&lt;https://github.com/GiantSteps/giantsteps-key-dataset&gt;) 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 <em>Proceedings of the 16th International Society for Music Information Retrieval Conference</em>, 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 (&lt;http:\\www.beatport.com&gt;). 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). <em>Tonality Estimation in Electronic Dance Music: A Computational and Musically Informed Examination.</em> PhD Thesis. Universitat Pompeu Fabra, Barcelona.

This dataset is mainly intended to assess the performance of computational key estimation algorithms in electronic dance music subgenres.