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...

Full description

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
id USIMONBOL2_66a9094df0620c096de210c63afdfee0
oai_identifier_str oai:bonga.unisimon.edu.co:20.500.12442/5119
network_acronym_str USIMONBOL2
network_name_str Repositorio Digital USB
repository_id_str
dc.title.spa.fl_str_mv 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.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019-12
dc.date.accessioned.none.fl_str_mv 2020-04-17T17:09:11Z
dc.date.available.none.fl_str_mv 2020-04-17T17:09:11Z
dc.date.spa.fl_str_mv 17-04-2050
dc.type.spa.fl_str_mv article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.driver.spa.fl_str_mv article
dc.identifier.issn.spa.fl_str_mv 15480992
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12442/5119
dc.identifier.url.none.fl_str_mv https://www.inaoep.mx/~IEEElat/index.php/transactions/article/view/2359/362
identifier_str_mv 15480992
url https://hdl.handle.net/20.500.12442/5119
https://www.inaoep.mx/~IEEElat/index.php/transactions/article/view/2359/362
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_14cb
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_14cb
dc.format.mimetype.spa.fl_str_mv pdf
dc.publisher.spa.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.eng.fl_str_mv IEEE LATIN AMERICA TRANSACTIONS
dc.source.spa.fl_str_mv Vol. 17, N°. 12 (2019)
institution Universidad Simón Bolívar
bitstream.url.fl_str_mv https://bonga.unisimon.edu.co/bitstreams/03df2ae1-7b1a-4fa1-960d-beeb7d3fbe6c/download
https://bonga.unisimon.edu.co/bitstreams/6b5d7147-3334-4b2b-b18a-feaf25042e28/download
bitstream.checksum.fl_str_mv 4460e5956bc1d1639be9ae6146a50347
733bec43a0bf5ade4d97db708e29b185
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
repository.name.fl_str_mv Repositorio Digital Universidad Simón Bolívar
repository.mail.fl_str_mv repositorio.digital@unisimon.edu.co
_version_ 1814076155215478784
spelling 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. El Profesional de la Información, vol. 28 (2), e280212, 2019.Reisenzein, R. What is a definition of emotion? And are emotions mental-behavioral processes?. Social science information, vol. 46 (3), pp. 424-428, 2007Savolainen, R. The interplay ot affective and cognitive factors in information seeking and use. Comparing Kuhlthau’s and Nahl’s models. Journal of Documentation, vol. 71(1), pp. 175-197, 2014.Savolainen, R. Expressing emotions in information sharing: a study of online discussion about immigration. Information Research, vol. 20 (1), 2015. http://InformationR. net/ir/20-1/paper662.html (2015-07-28)Platero Gómez, M, y Ortoll Espinet, E. El factor emocional en la búsqueda de información. Ibersid, vol. 10 (1), pp. 23-32, 2016Liu, Y; Sourina, O, y Nguyen M. K. Real-Time EEG-Based Emotion Recognition and Its Applications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6670 (20), pp. 256-277, 2011Sourin, O; Kulish, V, y Sourin, A. Novel Tools for Quantification of Brain Responses to Music Stimuli. IFMBE Proceedings, vol. 23 (13), pp. 411-414, 2009.You, M; Liu, J; Li,G.-Z, y Chen Y. Embedded Feature Selection for Multi-label Classification of Music Emotions. International Journal of Computational Intelligence Systems, vol. 5 (4), pp. 668-678, 2012.Kantono, K; Hamid, N; Shepherd, D; Lin, Y. H. T; Brard, C; Grazioli, G, y Thomas Carr, B. The effect of music on gelato perception in different eating contexts. Food Research International, vol. 113 (1), pp. 43-56, 2018Duisberg, R. A. Affective Modeling in Behavioral Simulations: Experience and Implementations. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3784 (1), pp. 498-504, 2005.Akdemir Akar, S; Kara, S; Agambayev, S, y Bilgiç, V. Nonlinear analysis of EEGs of patients with major depression during different emotional states. Computers in Biology and Medicine, vol. 67 (1), pp. 49-60, 2015.Hegde, S; Kumar, P. S; Rai, P; Mathur, G. N, y Varadan, V. K. Music close to one's heart - Heart rate variability with music, diagnostic with e-bra and smartphone. Proceedings of SPIE - The International Society for Optical Engineering, vol. 8344, (1), p. 1, 2012.Bai, J; Luo, K; Peng, J; Shi, J; Wu, Y; Feng, L; Li, J, y Wang, Y. Music emotions recognition by cognitive classification methodologies. Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, vol. 14 (16), pp. 121-129, 2017.Takahashi, Y; Hochin, T, .y Nomiya, H. Relationship between mental states with strong emotion aroused by music pieces and their feature values. Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, vol. 1 (1), pp. 718-725, 2014.Yeh C.H; Tseng W.Y; Chen C.Y; Lin Y.D; Tsai Y.R; Bi H.I; Lin Y.C, y Lin H.Y. Popular music representation: chorus detection & emotion recognition. Multimedia Tools and Applications, vol. 73 (3), pp. 2103- 2128, 2014.Mokhsin M. B; Rosli N. B; Wan Adnan W. A, y Abdul Manaf, N. Automatic music emotion classification using artificial neural network based on vocal and instrumental sound timbres. Frontiers in Artificial Intelligence and Applications, vol. 265 (13), pp. 3-14, 2014.Sanchez, P, y Garcia J. A New Methodology for Neural Network Training Ensures Error Reduction in Time Series Forecasting. Journal of Computer Sciences, vol. 13 (7,) pp. 211-217, 2017.Garcia, J, y Sanchez, P. 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. AI Magazine, vol. 17 (3), p. 37, 1996.Schneider, C.E; Hunter, E.G, y Bardach, S.H. Potential Cognitive Benefits From Playing Music Among Cognitively Intact Older Adults: A Scoping Review. Journal of Applied Gerontology, vol. 38(12), pp. 1763-1783, 2019.Delbouys, R; Hennequin, R; Piccoli, F; Royo-Letelier, J, y Moussallam, M. Music mood detection based on audio and lyrics with deep neural net. Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 20182018, pp. 370-375, 2018.Shastri, S, Mansotra, V. KDD-Based Decision Making: A Conceptual Framework Model for Maternal Health and Child Immunization Databases. Advances in Intelligent Systems and Computing, vol. 924, pp. 243-253, 2019.Lounes N; Oudghiri H; Chalal R, y Hidouci WK. From KDD to KUBD: Big Data Characteristics Within the KDD Process Steps. In: Rocha Á., Adeli H., Reis L., Costanzo S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 746, 2018.Shu-Hsien, L; Pei-Hui, C, y Pei-Yuan, H. Data mining techniques and applications – A decade review from 2000 to 2011. Expert Systems with Applications, vol. 39 (12), pp. 11303-11311, 2012.Sumathi, S, y Sivanandam, S. Introduction to Data Mining and its Applications.Studies in Computational Intelligence. Heidelberg: Springer-Verlag, 2006.Klösgen, W, y Zytkow J. Handbook of Data Mining and Knowledge Discovery. New York: Oxford University Press, Inc., 2002.Peña-Ayala A. Educational Data Mining: A Survey and A Data MiningBased Analysis of Recent Works. Expert Systems with Applications, vol. 41 (4-1), pp. 1432-1462, 2014.Barraza G. Sistema de detección de estados emocionales de usuarios según canciones escuchadas (Tesis). Barranquilla: Universidad Simón Bolívar, 2016.Londoño González B, y Sánchez, P. Algoritmo Novedoso Para la Detección de Tareas Repetitivas en el Teclado. Investigación e Innovación en Ingenierías, vol. 3 (2), jul. 2015CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/03df2ae1-7b1a-4fa1-960d-beeb7d3fbe6c/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/6b5d7147-3334-4b2b-b18a-feaf25042e28/download733bec43a0bf5ade4d97db708e29b185MD5320.500.12442/5119oai:bonga.unisimon.edu.co:20.500.12442/51192024-08-14 21:54:07.401http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalmetadata.onlyhttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.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

Publicaciones similares