Anna Aljanaki
2024
Genre-Conformity in the Topics of Lyrics and Song Popularity
Anna Aljanaki
Proceedings of the 3rd Workshop on NLP for Music and Audio (NLP4MusA)
The genre of a song defines both musical (rhythmic, timbral, performative) aspects of a song, but also the themes of lyrics and the style of writing. The audience has certain expectations as to emotional and thematic content of the genre they listen to. In this paper we use Music4All database to investigate whether breaking these expectations influences song popularity. We use topic modeling to divide song lyrics into 36 clusters, and apply tag clustering to separate the songs into 15 musical genres. We observe that in some genres (metal, hip-hop) lyrics are mostly written in specific topics, whereas in other genres they are spread over most topics. In most genres, songs that have lyrics that are not representative of the genre, are more popular than songs with genre-conforming lyrics.
2020
Computational Linguistics Metrics for the Evaluation of Two-Part Counterpoint Generated with Neural Machine Translation
Stefano Kalonaris
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Thomas McLachlan
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Anna Aljanaki
Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)