Million Song Dataset Subset
Thierry Bertin-Mahieux and Daniel P.W. Ellis and Brian Whitman and Paul Lamere

@article{,
title= {Million Song Dataset Subset},
keywords= {MillionSongSubset},
journal= {},
author= {Thierry Bertin-Mahieux and Daniel P.W. Ellis and Brian Whitman and Paul Lamere},
year= {},
url= {http://labrosa.ee.columbia.edu/millionsong/},
license= {},
abstract= {To let you get a feel for the dataset without committing to a full download, we also provide a subset consisting of 10,000 songs (1%, 1.8 gb) selected at random. It contains "additional files" (SQLite databases) in the same format as those for the full set, but referring only to the 10K song subset. Therefore, you can develop code on the subset, then port it to the full dataset.

The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks.

Its purposes are:
To encourage research on algorithms that scale to commercial sizes
To provide a reference dataset for evaluating research
As a shortcut alternative to creating a large dataset with APIs (e.g. The Echo Nest's)
To help new researchers get started in the MIR field
The core of the dataset is the feature analysis and metadata for one million songs, provided by The Echo Nest. The dataset does not include any audio, only the derived features. Note, however, that sample audio can be fetched from services like 7digital, using code we provide.

Please cite the following paper:
Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. 
The Million Song Dataset. In Proceedings of the 12th International Society
for Music Information Retrieval Conference (ISMIR 2011), 2011.

==Acknowledgements
The Million Song Dataset was created under a grant from the National Science Foundation, project IIS-0713334. The original data was contributed by The Echo Nest, as part of an NSF-sponsored GOALI collaboration. Subsequent donations from SecondHandSongs.com, musiXmatch.com, and last.fm, as well as further donations from The Echo Nest, are gratefully acknowledged.

Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.

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