Two data sets for tempo estimation and key detection in electronic dance music annotated from user corrections

Abstract

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.