GiantSteps+ EDM Key Dataset

The GiantSteps+ EDM Key Dataset 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 (<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.

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