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Greek Lyrics Generation
This paper documents the efforts in implementing lyric generation machine learning models in the Greek language for the genre of Éntekhno music. To accomplish this, we used three different Long Short-Term Memory Recurrent Neural Network approaches. The first method utilizes word-level bi-directional...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256574/ http://dx.doi.org/10.1007/978-3-030-49186-4_37 |
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author | Lampridis, Orestis Kefalas, Athanasios Tzallas, Petros |
author_facet | Lampridis, Orestis Kefalas, Athanasios Tzallas, Petros |
author_sort | Lampridis, Orestis |
collection | PubMed |
description | This paper documents the efforts in implementing lyric generation machine learning models in the Greek language for the genre of Éntekhno music. To accomplish this, we used three different Long Short-Term Memory Recurrent Neural Network approaches. The first method utilizes word-level bi-directional network models, the second method expands on the first by learning the word embeddings on the initial layer of the network, while the last method is based on a char-level network model. Our experimental procedure, which utilized a high sample of human judges, shows that texts of lyrics generated by our models are of high quality and are not that easily distinguishable from actual lyrics. |
format | Online Article Text |
id | pubmed-7256574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72565742020-05-29 Greek Lyrics Generation Lampridis, Orestis Kefalas, Athanasios Tzallas, Petros Artificial Intelligence Applications and Innovations Article This paper documents the efforts in implementing lyric generation machine learning models in the Greek language for the genre of Éntekhno music. To accomplish this, we used three different Long Short-Term Memory Recurrent Neural Network approaches. The first method utilizes word-level bi-directional network models, the second method expands on the first by learning the word embeddings on the initial layer of the network, while the last method is based on a char-level network model. Our experimental procedure, which utilized a high sample of human judges, shows that texts of lyrics generated by our models are of high quality and are not that easily distinguishable from actual lyrics. 2020-05-06 /pmc/articles/PMC7256574/ http://dx.doi.org/10.1007/978-3-030-49186-4_37 Text en © IFIP International Federation for Information Processing 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Lampridis, Orestis Kefalas, Athanasios Tzallas, Petros Greek Lyrics Generation |
title | Greek Lyrics Generation |
title_full | Greek Lyrics Generation |
title_fullStr | Greek Lyrics Generation |
title_full_unstemmed | Greek Lyrics Generation |
title_short | Greek Lyrics Generation |
title_sort | greek lyrics generation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256574/ http://dx.doi.org/10.1007/978-3-030-49186-4_37 |
work_keys_str_mv | AT lampridisorestis greeklyricsgeneration AT kefalasathanasios greeklyricsgeneration AT tzallaspetros greeklyricsgeneration |