MusicOSet: An Enhanced Open Dataset for Music Data Mining

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). To create MusicOSet, the potential information sources were divided into three main categories: music popularity sources, metadata sources, and acoustic and lyrical features sources. Data from all three categories were initially collected between January and May 2019. Nevertheless, the update and enhancement of the data happened in June 2019.

The attractive features of MusicOSet include:

Integration and centralization of different musical data sources

Calculation of popularity scores and classification of hits and non-hits musical elements, varying from 1962 to 2018

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

Availability of acoustic and lyrical resources

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

| Data | # Records |
|:-----------------:|:---------:|
| Songs | 20,405 |
| Artists | 11,518 |
| Albums | 26,522 |
| Lyrics | 19,664 |
| Acoustic Features | 20,405 |
| Genres | 1,561 |