Logotipo del repositorio
Logotipo del repositorio
 

Knowledge discovery in musical databases for moods detection

Vista sencilla de ítem
dc.contributor.authorSánchez, P.
dc.contributor.authorCano, J.
dc.contributor.authorGarcía, D.
dc.contributor.authorPinzon, A.
dc.contributor.authorRodriguez, G.
dc.contributor.authorGarcía- González, J.
dc.contributor.authorPerez, L.
dc.date17-04-2050spa
dc.date.accessioned2020-04-17T17:09:11Z
dc.date.available2020-04-17T17:09:11Z
dc.date.issued2019-12
dc.description.abstractIn 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.spa
dc.format.mimetypepdfspa
dc.identifier.issn15480992spa
dc.identifier.urihttps://hdl.handle.net/20.500.12442/5119
dc.identifier.urlhttps://www.inaoep.mx/~IEEElat/index.php/transactions/article/view/2359/362
dc.language.isoengspa
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)spa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceIEEE LATIN AMERICA TRANSACTIONSeng
dc.sourceVol. 17, N°. 12 (2019)spa
dc.subjectData miningeng
dc.subjectKnowledge discoveryeng
dc.subjectDatabases processeng
dc.subjectMusiceng
dc.subjectPredictioneng
dc.subjectData Analysiseng
dc.titleKnowledge discovery in musical databases for moods detectionspa
dc.typearticlespa
dc.type.driverarticlespa
dcterms.referencesSarabia-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.eng
dcterms.referencesReisenzein, R. What is a definition of emotion? And are emotions mental-behavioral processes?. Social science information, vol. 46 (3), pp. 424-428, 2007eng
dcterms.referencesSavolainen, 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.eng
dcterms.referencesSavolainen, 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)eng
dcterms.referencesPlatero Gómez, M, y Ortoll Espinet, E. El factor emocional en la búsqueda de información. Ibersid, vol. 10 (1), pp. 23-32, 2016eng
dcterms.referencesLiu, 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, 2011eng
dcterms.referencesSourin, 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.eng
dcterms.referencesYou, 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.eng
dcterms.referencesKantono, 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, 2018eng
dcterms.referencesDuisberg, 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.eng
dcterms.referencesAkdemir 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.eng
dcterms.referencesHegde, 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.eng
dcterms.referencesBai, 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.eng
dcterms.referencesTakahashi, 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.eng
dcterms.referencesYeh 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.eng
dcterms.referencesMokhsin 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.eng
dcterms.referencesSanchez, 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.eng
dcterms.referencesGarcia, 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.eng
dcterms.referencesBarraza G. Sistema de detección de estados emocionales de usuarios según canciones escuchadas (Tesis). Barranquilla: Universidad Simón Bolívar, 2016.spa
dcterms.referencesLondoño 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. 2015spa
dcterms.referencesInternational 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].eng
dcterms.referencesInternational 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].eng
dcterms.referencesZentner, 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, 2008eng
dcterms.referencesFayyad, U; Piatetsky-Shapiro, G; y Smyth, P. From data mining to knowledge discovery in databases. AI Magazine, vol. 17 (3), p. 37, 1996.eng
dcterms.referencesSchneider, 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.eng
dcterms.referencesDelbouys, 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.eng
dcterms.referencesShastri, 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.eng
dcterms.referencesLounes 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.eng
dcterms.referencesShu-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.eng
dcterms.referencesSumathi, S, y Sivanandam, S. Introduction to Data Mining and its Applications.Studies in Computational Intelligence. Heidelberg: Springer-Verlag, 2006.eng
dcterms.referencesKlösgen, W, y Zytkow J. Handbook of Data Mining and Knowledge Discovery. New York: Oxford University Press, Inc., 2002.eng
dcterms.referencesPeña-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.eng
dcterms.references
oaire.versioninfo:eu-repo/semantics/publishedVersionspa

Archivos

Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
381 B
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Artículos

Universidad Simón Bolívar: Todos los derechos reservados / Sistema de biblioteca