Knowledge discovery in musical databases for moods detection
In this paper, methodology Knowledge discovery in databases is used in the design and implementation of a tool for moods detection from musical data. The application allows users to interact with a music player, and based on their playlist and musical genre, recognizes and classified their emotional...
- Autores:
-
Sánchez, P.
Cano, J.
García, D.
Pinzon, A.
Rodriguez, G.
García- González, J.
Perez, L.
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/5119
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/5119
https://www.inaoep.mx/~IEEElat/index.php/transactions/article/view/2359/362
- Palabra clave:
- Data mining
Knowledge discovery
Databases process
Music
Prediction
Data Analysis
- Rights
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Knowledge discovery in musical databases for moods detection |
| title |
Knowledge discovery in musical databases for moods detection |
| spellingShingle |
Knowledge discovery in musical databases for moods detection Data mining Knowledge discovery Databases process Music Prediction Data Analysis |
| title_short |
Knowledge discovery in musical databases for moods detection |
| title_full |
Knowledge discovery in musical databases for moods detection |
| title_fullStr |
Knowledge discovery in musical databases for moods detection |
| title_full_unstemmed |
Knowledge discovery in musical databases for moods detection |
| title_sort |
Knowledge discovery in musical databases for moods detection |
| dc.creator.fl_str_mv |
Sánchez, P. Cano, J. García, D. Pinzon, A. Rodriguez, G. García- González, J. Perez, L. |
| dc.contributor.author.none.fl_str_mv |
Sánchez, P. Cano, J. García, D. Pinzon, A. Rodriguez, G. García- González, J. Perez, L. |
| dc.subject.eng.fl_str_mv |
Data mining Knowledge discovery Databases process Music Prediction Data Analysis |
| topic |
Data mining Knowledge discovery Databases process Music Prediction Data Analysis |
| description |
In this paper, methodology Knowledge discovery in databases is used in the design and implementation of a tool for moods detection from musical data. The application allows users to interact with a music player, and based on their playlist and musical genre, recognizes and classified their emotional state using a neural network. The results found are promising to have an accuracy of more than 72,4%, in addition the developed tool allows the constant taking and storage of data, the analysis in real time and issues suggestions of songs to positively influence the current emotional state, so that a greater use of the application can guarantee better results. |
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2019 |
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2019-12 |
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2020-04-17T17:09:11Z |
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2020-04-17T17:09:11Z |
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17-04-2050 |
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article |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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15480992 |
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https://hdl.handle.net/20.500.12442/5119 |
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https://www.inaoep.mx/~IEEElat/index.php/transactions/article/view/2359/362 |
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https://hdl.handle.net/20.500.12442/5119 https://www.inaoep.mx/~IEEElat/index.php/transactions/article/view/2359/362 |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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pdf |
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Institute of Electrical and Electronics Engineers (IEEE) |
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IEEE LATIN AMERICA TRANSACTIONS |
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Vol. 17, N°. 12 (2019) |
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Universidad Simón Bolívar |
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Sánchez, P.23320ccb-57a6-4644-964a-3b5c1b9be77cCano, J.179f8ba8-672d-4502-bdbc-f000254f8a36García, D.4ed45301-976b-49c3-a67c-433a5df3f282Pinzon, A.dc7bb517-0ed0-4518-85f0-b75ad8ee713dRodriguez, G.534355e1-7911-4710-8d35-cc2afab828a1García- González, J.23cbefed-7ef8-4bad-b81a-ef63fa5b4431Perez, L.078e17e3-3a9c-49e0-9f73-9ca320ea9ea217-04-20502020-04-17T17:09:11Z2020-04-17T17:09:11Z2019-1215480992https://hdl.handle.net/20.500.12442/5119https://www.inaoep.mx/~IEEElat/index.php/transactions/article/view/2359/362In this paper, methodology Knowledge discovery in databases is used in the design and implementation of a tool for moods detection from musical data. The application allows users to interact with a music player, and based on their playlist and musical genre, recognizes and classified their emotional state using a neural network. The results found are promising to have an accuracy of more than 72,4%, in addition the developed tool allows the constant taking and storage of data, the analysis in real time and issues suggestions of songs to positively influence the current emotional state, so that a greater use of the application can guarantee better results.pdfengInstitute of Electrical and Electronics Engineers (IEEE)Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_14cbIEEE LATIN AMERICA TRANSACTIONSVol. 17, N°. 12 (2019)Data miningKnowledge discoveryDatabases processMusicPredictionData AnalysisKnowledge discovery in musical databases for moods detectionarticlearticlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Sarabia-Sánchez, F.J; Aguado, J.M; Martínez-Martínez, I.J. Privacy paradox in the mobile environment: The influence of the emotions. 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Autoregressive Moving Average Recurrent Neural Networks Applied to the Modelling of Colombian Exchange Rate. International Journal Of Artificial Intelligence, vol. 16 (2), pp. 194-207, 2018.International Federation of the Phonographic Industry, «Informe sobre los habitos de consumo de musica,» 1 Septiembre 2017. [En línea]. Available: https://www.ifpi.org/downloads/MCIR_Spanish.pdf. [Último acceso: 1 Octubre 2018].International Federation of the Phonographic Industry, «Informe mundial de la música,» 1 Enero 2017. [Online]. Available: https://www.ifpi.org/downloads/GMR2016_Spanish.pdf. [Último acceso: 1 Octubre 2018].Zentner, M; Grandjean, D, y Scherer, K. R. Emotions Evoked by the Sound of Music: Characterization, Classification and Measurement. American Psychological Association, vol. 8 (4), pp. 494–521, 2008Fayyad, U; Piatetsky-Shapiro, G; y Smyth, P. From data mining to knowledge discovery in databases. 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