Lyrics Segmentation: Textual Macrostructure Detection using Convolutions

Michael Fell, Yaroslav Nechaev, Elena Cabrio, Fabien Gandon

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Abstract
Lyrics contain repeated patterns that are correlated with the repetitions found in the music they accompany. Repetitions in song texts have been shown to enable lyrics segmentation – a fundamental prerequisite of automatically detecting the building blocks (e.g. chorus, verse) of a song text. In this article we improve on the state-of-the-art in lyrics segmentation by applying a convolutional neural network to the task, and experiment with novel features as a step towards deeper macrostructure detection of lyrics.
Anthology ID:
C18-1174
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2044–2054
Language:
URL:
https://aclanthology.org/C18-1174
DOI:
Bibkey:
Cite (ACL):
Michael Fell, Yaroslav Nechaev, Elena Cabrio, and Fabien Gandon. 2018. Lyrics Segmentation: Textual Macrostructure Detection using Convolutions. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2044–2054, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Lyrics Segmentation: Textual Macrostructure Detection using Convolutions (Fell et al., COLING 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/C18-1174.pdf
Code
 TuringTrain/lyrics_segmentation