An Enhanced Music Dataset for Music Data Mining

We introduce the MusicOSet, an open and enhanced dataset of musical elements (music, albums, and artists) suitable for music data mining. The attractive features of MusicOSet include the enrichment of existing metadata to which it is linked and the popularity classification of the musical elements present in the dataset.

Awesome Features

Easy Accessible

Unrestricted access in two formats (SQL database and compressed .csv files).


Integration and centralization of different musical data sources.

Popularity Information

Popularity scores and classification of hits and non-hits musical elements.

Enriched Metadata

Enriched metadata for music, artists, and albums from the US popular music industry.

Acoustic Resources

Availability of acoustic fingerprints collected directly from Spotify.

Lyrical Resources

Availability of lyrics resources collected using the LyricsGenius library.










Content Description


Information about the followers of the artist.

The popularity of the artist. The value will be between 0 and 100, with 100 being the most popular. The artist’s popularity is calculated from the popularity of all the artist’s tracks.

The type of the artists: one of "singer", "band", "duo" or "rapper".

The main genre in which the artist is associated with.

A list of the genres the artist is associated with. For example: "Prog Rock", "Post-Grunge". (If not yet classified, the array is empty.


The artists of the album.

The popularity of the album. The value will be between 0 and 100, with 100 being the most popular. The popularity is calculated from the popularity of the album’s individual tracks.

The total number of tracks.

The type of the album: one of "album", "single" or "compilation".


The artists who performed the track.

The popularity of the track. The value will be between 0 and 100, with 100 being the most popular. The popularity of a track is a value between 0 and 100, with 100 being the most popular. The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are. Generally speaking, songs that are being played a lot now will have a higher popularity than songs that were played a lot in the past. Duplicate tracks (e.g. the same track from a single and an album) are rated independently. Artist and album popularity is derived mathematically from track popularity. Note that the popularity value may lag actual popularity by a few days: the value is not updated in real time.

Whether or not the track has explicit lyrics true = yes it does; false = no it does not OR unknown.

The type of the song: one of "solo songs" (with only one artist present in its execution) or "collaborative songs" (where there is more than one artist).

Acoustic Features

The estimated overall key of the track. Integers map to pitches using standard Pitch Class notation. If no key was detected, the value is -1.

Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.

An estimated overall time signature of a track. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure).

A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.

Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.

Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.

Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.

Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.

The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.

Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.

A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.


A broad variety of music data mining tasks could be performed and analyzed using the MusicOSet.

Metadata Analysis.

Metadata analysis may involve, for example, music visualization, association mining, clustering, etc.

Read More

Hit Song Science.

In Hit Song Science (HSS), researchers seek to identify features that make music more likely to be popular.

Read More

Music Information Retrieval.

Music Information Retrieval (MIR) is an emerging research area dedicated to meeting users’ musical information needs.

Read More


MusicOSet is an open and enhanced dataset of musical elements (artists, songs and albums) based on musical popularity classification. Provides a directly accessible collection of data suitable for numerous tasks in music data mining (e.g., data visualization, classification, clustering, similarity search, MIR, HSS and so forth). MusicOSet is available in a public repository in two different formats:

Relational Database


SQL file that will create the relational database and subsequently loads all the information in the tables by a MySQL installation (233MB)

.csv Tables

Contains textual and numeric information about songs, artists, and albums

Contains nine tables of musical popularity information

Contains lyrics and acoustic fingerprints of the songs collected


The work is supported by CNPq, Brazil.