The WASABI Dataset: Cultural, Lyrics and Audio Analysis Metadata About 2 Million Popular Commercially Released Songs

Michel Buffa (1) , Elena Cabrio (1) , Michael Fell (1) , Fabien Gandon (1) , Alain Giboin (1) , Romain Hennequin (2) , Franck Michel (1) , Johan Pauwels (3) , Guillaume Pellerin (4) , Maroua Tikat (1) , Marco Winckler (1)

1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
2 Deezer Research
3 QMUL - Queen Mary University of London
4 IRCAM - Institut de Recherche et Coordination Acoustique/Musique

Since 2017, the goal of the two-million song WASABI database has been to build a knowledge graph linking collected metadata (artists,discography, producers, dates, etc.) with metadata generated by the analysis of both the songs’ lyrics (topics, places, emotions, structure, etc.) and audio signal (chords, sound, etc.). It relies on natural language processing and machine learning methods for extraction, and semantic Web frameworks for representation and integration. It describes more than 2 millions commercial songs, 200K albums and 77K artists. It can be exploited by music search engines, music professionals (e.g. journalists, radio presenters, music teachers) or scientists willing to analyze popular music published since 1950. It is available under an open license, inmultiple formats and with online and open source services including aninteractive navigator, a REST API and a SPARQL endpoint.

Mots clés: Music metadata; Lyrics analysis; Named entites; Linked data

Domaines: Base de données [cs.DB]

Michel Buffa, Elena Cabrio, Michael Fell, Fabien Gandon, Alain Giboin, et al.. The WASABI Dataset: Cultural, Lyrics and Audio Analysis Metadata About 2 Million Popular Commercially Released Songs. The Semantic Web. ESWC 2021. Lecture Notes in Computer Science, vol 12731., pp.515-531, 2021, ⟨10.1007/978-3-030-77385-4_31⟩. ⟨hal-03282619⟩