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FMA: A Dataset For Music Analysis Data Set
Download: Data Folder, Data Set Description

Abstract: FMA features 106,574 tracks and includes song title, album, artist, genres; play counts, favorites, comments; description, biography, tags; together with audio (343 days, 917 GiB) and features.

Data Set Characteristics:  

Multivariate, Time-Series

Number of Instances:




Attribute Characteristics:


Number of Attributes:


Date Donated


Associated Tasks:

Classification, Clustering

Missing Values?


Number of Web Hits:



Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson, EPFL LTS2.

Data Set Information:

* Audio track (encoded as mp3) of each of the 106,574 tracks. It is on average 10 millions samples per track.
* Nine audio features (consisting of 518 attributes) for each of the 106,574 tracks.
* Given the metadata, multiple problems can be explored: recommendation, genre recognition, artist identification, year prediction, music annotation, unsupervized categorization.
* The dataset is split into four sizes: small, medium, large, full.
* Please see the paper and the GitHub repository for more information ([Web Link])

Attribute Information:

Nine audio features computed across time and summarized with seven statistics (mean, standard deviation, skew, kurtosis, median, minimum, maximum):
1. Chroma, 84 attributes
2. Tonnetz, 42 attributes
3. Mel Frequency Cepstral Coefficient (MFCC), 140 attributes
4. Spectral centroid, 7 attributes
5. Spectral bandwidth, 7 attributes
6. Spectral contrast, 49 attributes
7. Spectral rolloff, 7 attributes
8. Root Mean Square energy, 7 attributes
9. Zero-crossing rate, 7 attributes

Relevant Papers:


Citation Request:

Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson. FMA: A Dataset For Music Analysis. [Web Link], 2017.

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 In Collaboration With:

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