Recording Similarity

An emerging feature in AcousticBrainz is recording similarity.

We compute similarity between recordings based on a selection of high and low-level features [metrics].

Similarity metrics for each recording should be computed on submission. When the metrics for a recording have been added to static index files, we can query for track-track similarity based on our choice of feature.

This dataset is made available through a set of API endpoints, and distribution of the static index files for more flexibility.

Similarity Metrics

Similarity metrics are methods of applying a vectorized measurement to one, or many features of high and low-level data.

Thus far, the following metrics can be used to assess similarity:

Metric Hybrid Category
MFCCs False Timbre
MFCCs (Weighted) False Timbre
GFCCs False Timbre
GFCCs (Weighted) False Timbre
Key False Rhythm (Key/Scale)
BPM False Rhythm
OnsetRate False Rhythm
Moods False High-Level
Instruments False High-Level
Dortmund False High-Level (Genre)
Rosamerica False High-Level (Genre)
Tzanetakis False High-Level (Genre)

Note: Hybrid metrics are combinations of multiple metrics. In the future, we hope to integrate more metrics that combine low-level features for a more holistic approach to similarity.

Similarity Statistics

Some of our metrics are normalized using the mean and standard deviation of their features from the lowlevel table. We must compute the statistics for such metrics prior to computing the metrics themselves. To do so, we collect a sample of the lowlevel table and use it to approximate the mean and standard deviation. Currently, the metrics that require statistics are the following:

  • MFCCs
  • Weighted MFCCs
  • GFCCs
  • Weighted GFCCs


Metric vectors for each recording are added to static indices, created with the Annoy library. An individual index exists for each of the metrics available. An index uses a nearest neighbours approach for queries.

Note that computing an index takes time, and thus it cannot happen each time a recording is submitted. Indices are recomputed, including new submissions, on a time interval.

More can be read about indices and contributing to similarity work in the developer reference.


The similarity engine is an ongoing project, and its results are still largely experimental. While we tune different index parameters (described in the developer reference), we’d like to gather feedback from the community.

As such, we’ve made an evaluation available to the public. When viewing the summary data for a recording, you may access similar recordings organized by metric. Recordings are organized from most similar to least similar. Alongside the list of the most similar recordings, you may provide input:

  • Rate whether the recording should be higher or lower on the list of similarity, or whether this result seems accurate.
  • Provide additional suggestions related to a specific similar recording, or in general.

Feel free to provide as much or as little feedback as you wish when browsing. We appreciate your help in improving similarity at AcousticBrainz!