{"id":111450,"date":"2018-03-11T10:37:56","date_gmt":"2018-03-11T10:37:56","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/automatic-classification-of-musical-mood-by-content-based-analysis\/"},"modified":"2018-03-11T10:37:56","modified_gmt":"2018-03-11T10:37:56","slug":"automatic-classification-of-musical-mood-by-content-based-analysis","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/inteligencia-artificial\/automatic-classification-of-musical-mood-by-content-based-analysis\/","title":{"rendered":"Automatic classification of musical mood by content-based analysis"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Cyril Laurier <\/strong><\/h2>\n<p>La m\u00fasica en formato digital forma parte de nuestras vidas. Automatizar la organizaci\u00f3n de estos datos es un gran desaf\u00edo. En esta tesis, nos centramos en la clasificaci\u00f3n autom\u00e1tica de m\u00fasica a partir de la detecci\u00f3n de la emoci\u00f3n que comunica. Para conseguirlo, proponemos modelos usando informaciones extra\u00eddas de la se\u00f1al de audio mediante t\u00e9cnicas de procesamiento de se\u00f1ales, aprendizaje autom\u00e1tico y recuperaci\u00f3n de informaci\u00f3n. Primero, estudiamos c\u00f3mo los miembros de una red social utilizan etiquetas y palabras clave para describir la m\u00fasica y las emociones que evoca. Con una t\u00e9cnica para reducir la complejidad dimensional de este problema, encontramos un modelo para representar los estados de \u00e1nimo. Luego, proponemos un m\u00e9todo de clasificaci\u00f3n autom\u00e1tica de emociones y detallamos los resultados para distintos tipos de clasificadores. Analizamos las contribuciones de descriptores de audio y c\u00f3mo sus valores est\u00e1n relacionados con los estados de \u00e1nimo, intentando encontrar explicaciones desde un punto de vista psicol\u00f3gico y\/o music\u00f3logico. Proponemos tambi\u00e9n una versi\u00f3n multimodal de nuestro algoritmo, usando las letras de canciones con un nuevo m\u00e9todo de clasificaci\u00f3n basado en las palabras claves para distinguir categor\u00edas de emociones. Finalmente, despu\u00e9s de estudiar la relaci\u00f3n entre el estado de \u00e1nimo y el g\u00e9nero musical, presentamos un m\u00e9todo usando la clasificaci\u00f3n autom\u00e1tica por g\u00e9nero. mostramos que clasificadores basados en el g\u00e9nero obtienen mejores resultados que otros m\u00e9todos est\u00e1ndar. A modo de recapitulaci\u00f3n conceptual y algor\u00edtmica, proponemos una t\u00e9cnica de extracci\u00f3n de reglas para entender como los algoritmos de aprendizaje autom\u00e1tico predicen la emoci\u00f3n evocada por la m\u00fasica. Nuestros algoritmos han sido evaluados con datos de usuarios y en concursos de evaluaci\u00f3n internacionales.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Automatic classification of musical mood by content-based analysis<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Automatic classification of musical mood by content-based analysis <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Cyril Laurier <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Pompeu fabra<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 19\/10\/2011<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Xavier Serra Casals<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: petri Toiviainen <\/li>\n<li>geoffroy Peeters (vocal)<\/li>\n<li>  (vocal)<\/li>\n<li>  (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Cyril Laurier La m\u00fasica en formato digital forma parte de nuestras vidas. Automatizar la organizaci\u00f3n de estos [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[5263,2528,9132,18712,39060],"tags":[222056,222058,222057,38388],"class_list":["post-111450","post","type-post","status-publish","format-standard","hentry","category-emocion","category-inteligencia-artificial","category-musica-y-musicologia","category-pompeu-fabra","category-tratamiento-de-senales","tag-cyril-laurier","tag-geoffroy-peeters","tag-petri-toiviainen","tag-xavier-serra-casals"],"_links":{"self":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/111450","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/comments?post=111450"}],"version-history":[{"count":0,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/111450\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/media?parent=111450"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/categories?post=111450"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/tags?post=111450"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}