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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...

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Detalles Bibliográficos
Autores principales: Lampridis, Orestis, Kefalas, Athanasios, Tzallas, Petros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
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.
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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
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