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

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-03282619
Contributor : Franck Michel Connect in order to contact the contributor
Submitted on : Friday, July 9, 2021 - 3:50:35 PM
Last modification on : Friday, August 5, 2022 - 3:50:44 AM
Long-term archiving on: : Sunday, October 10, 2021 - 7:44:40 PM

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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⟩

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