AcousticBrainz data

Before diving into the API details, please have a look at the sample data below.

Getting data

To fetch the JSON documents for a given MusicBrainz recording ID, you can construct the following endpoints for low-level and high-level:

   GET https://acousticbrainz.org/api/v1/<MBID>/low-level
   GET https://acousticbrainz.org/api/v1/<MBID>/high-level

Examples:

   GET https://acousticbrainz.org/api/v1/96685213-a25c-4678-9a13-abd9ec81cf35/low-level
   GET https://acousticbrainz.org/api/v1/96685213-a25c-4678-9a13-abd9ec81cf35/high-level

You can find out how many low-level documents we have for a given recording using the following endpoint:

   GET https://acousticbrainz.org/api/v1/<MBID>/count

Example:

   GET https://acousticbrainz.org/api/v1/96685213-a25c-4678-9a13-abd9ec81cf35/count

If there's more than one document, you can specify a number argument n:

   GET https://acousticbrainz.org/api/v1/<MBID>/low-level?n=1

Documents are counted from 0 to count-1.

Example:

   GET https://acousticbrainz.org/api/v1/96685213-a25c-4678-9a13-abd9ec81cf35/low-level?n=1

Submitting data

The submit API endpoint is:

   POST https://acousticbrainz.org/api/v1/<MBID>/low-level

It expects a JSON payload, containing the output of a feature extractor (see the contribute page).

High-level models and dataset

AcousticBrainz includes models to compute semantically meaningful music features. You can read more about the Accuracies and confusion matrices for default models

Additionally, we provide the functionality to design your own datasets and use them to build new models that can be integrated into the data that AcousticBrainz computes.

Sample data

AcousticBrainz stores two levels of data: low-level and high-level.

Both types of information are JSON documents, two documents per each MusicBrainz ID for a recording.

The low-level data includes acoustic descriptors characterizing overall loudness, dynamics and spectral shape of a signal, rhythm descriptors (including beats per minute), and tonal information (including keys and scales). The low-level data is stable, compared to the higher-level data. This hopefully means that we will not have to recompute this data from audio files too frequently. More detailed information about the low-level data is provided in the official documentation for Essentia toolkit.


{
   "tonal" : {
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         "dvar2" : 2.08324654523e-05,
         "min" : 0.2879781425,
         "max" : 0.735961318016,
         "dvar" : 2.50228695222e-05,
         "dmean" : 0.00604996597394,
         "median" : 0.529873728752,
         "mean" : 0.524879217148,
         "dmean2" : 0.00469066808,
         "var" : 0.00976115558296
      },
      "key_key" : "C#",
      "thpcp" : [
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      "chords_scale" : "minor",
      "chords_changes_rate" : 0.0229936875403,
      "chords_key" : "C#",
      "hpcp" : {
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}

The high-level data includes information about moods, genres, vocals and music type automatically inferred from low-level data by pre-trained classifiers. The high-level data can be computed from the low-level data, without requiring access to original audio files. The high-level data is less stable than the low-level data, since it contains more experimental algorithms. We anticipate re-calculating the high-level data until it becomes fully stable.


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