Singability-enhanced lyric generator with music style transfer
| Autor: | Jason C. Hung, Jia-Wei Chang, Kuan-Cheng Lin |
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| Rok vydání: | 2021 |
| Předmět: |
Parsing
Computer Networks and Communications Rhyme Computer science business.industry media_common.quotation_subject 020206 networking & telecommunications Context (language use) 02 engineering and technology Musical Lyrics computer.software_genre Style (sociolinguistics) Originality 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Singing business computer Natural language processing media_common |
| Zdroj: | Computer Communications. 168:33-53 |
| ISSN: | 0140-3664 |
| DOI: | 10.1016/j.comcom.2021.01.002 |
| Popis: | The lyrics generator should consider the context and the singability of the songs because every song expresses a story through the context of lyrics, and the lyrics should sound with the music well. Therefore, this study proposes a framework to generate the singable lyrics, and the context of lyrics should fit the given musical style. For the context, this study adopts the GPT-2 model which is powerful for text generation. The conditional GPT-2 model can be used to generate lyrics according to the given style. For suitable for singing, this study adjusts the structure and rhyme of lyrics through the use of a syntactic parser and a rhyme modification module. With automatic and human evaluations, the experimental results show that the proposed method can generate lyrics with high structural consistency, rhyme consistency, and originality according to the given music style. |
| Databáze: | OpenAIRE |
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