FMA: A Dataset For Music Analysis

Donated on 5/23/2017

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.

Dataset Characteristics

Multivariate, Time-Series

Subject Area

Computer Science

Associated Tasks

Classification, Clustering

Feature Type

Real

# Instances

106574

# Features

-

Dataset Information

Additional 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 (https://github.com/mdeff/fma)

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
no
no
no
no
no
no
no
no
no
no

0 to 10 of 518

Additional Variable 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

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Creators

Michal Defferrard

Kirell Benzi

Pierre Vandergheynst

Xavier Bresson

Notes

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