Thursday , October 22 2020

Million Song Dataset, Hacker News

TheMillion Song Datasetis 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 like7digital, usingCodewe 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 betweenThe Echo NestandLabROSA. It was supported in part by the NSF.

How to get started

To get a sense of the dataset, you can look at thisdescriptionof one of the million songs.

To start your own experiments, you candownloadthe entire dataset ( (GB). We also provide asubsetof 10, 000 songs (1%, 1.8 GB compressed) for a quick taste.

While waiting for the download, take a look at theFAQ, which includes a list of all the fields in the database.

We also have a set of suggestedtasks, including snippets of code to get you started.

Pleasecontact usif you have any questions about the dataset and how to use it. You can also try browsing and posting on ourforum(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 12 th 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,, and, 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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