Lyrics Segmentation: Textual Macrostructure Detection using Convolutions
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:
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/C18-1174.pdf
- Code
- TuringTrain/lyrics_segmentation