@inproceedings{thierry_bertin_mahieux_2018_1415820,
author= {Thierry Bertin-Mahieux and Daniel P. W. Ellis and Brian Whitman and Paul Lamere},
title= {The Million Song Dataset.},
booktitle= {{Proceedings of the 12th International Society for 
                   Music Information Retrieval Conference}},
year= {2018},
pages= {591-596},
publisher= {ISMIR},
month= {sep},
venue= {Miami, United States},
doi= {10.5281/zenodo.1415820},
url= {https://doi.org/10.5281/zenodo.1415820},
abstract= {We introduce the Million Song Dataset, a freely-available collection of audio features and metadata for a million con- temporary popular music tracks. We describe its creation process, its content, and its possible uses. Attractive fea- tures of the Million Song Database include the range of ex- isting resources to which it is linked, and the fact that it is the largest current research dataset in our field. As an illustra- tion, we present year prediction as an example application, a task that has, until now, been difficult to study owing to the absence of a large set of suitable data. We show positive results on year prediction, and discuss more generally the future development of the dataset.},
keywords= {million, song, dataset, music, features, metadata},
terms= {},
license= {},
superseded= {}
}