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.
The Million Song Dataset is also a cluster of complementary datasets contributed by the community:
The Million Song Dataset started as a collaborative project between The Echo Nest and LabROSA. It was supported in part by the NSF.
How to get started
To get a sense of the dataset, you can look at this description of one of the million songs.
To start your own experiments, you can download the entire dataset (280 GB). We also provide a subset of 10,000 songs (1%, 1.8 GB compressed) for a quick taste.
While waiting for the download, take a look at the FAQ, which includes a list of all the fields in the database.
We also have a set of suggested tasks, including snippets of code to get you started.
Please contact us if you have any questions about the dataset and how to use it. You can also try browsing and posting on our forum (registration required).
Using the dataset?
Please cite the following paper [pdf] [bib]:
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.
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.