{"id":111070,"date":"2018-03-11T10:37:27","date_gmt":"2018-03-11T10:37:27","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/a-statistical-pattern-recognition-approach-to-symbolic-music-classification\/"},"modified":"2018-03-11T10:37:27","modified_gmt":"2018-03-11T10:37:27","slug":"a-statistical-pattern-recognition-approach-to-symbolic-music-classification","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/inteligencia-artificial\/a-statistical-pattern-recognition-approach-to-symbolic-music-classification\/","title":{"rendered":"A statistical pattern recognition approach to symbolic music classification"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Pedro Jos\u00e9 Ponce De Le\u00f3n Amador <\/strong><\/h2>\n<p>El trabajo se enmarca en el dominio de recuperaci\u00f3n de m\u00fasica por ordenador, a partir de fuentes simb\u00f3licas (partituras digitales o similares). En concreto, se plantean soluciones computacionales mediante la aplicaci\u00f3n de t\u00e9cnicas estad\u00edsticas de reconocimiento de formas a dos problemas: la selecci\u00f3n autom\u00e1tica de partes mel\u00f3dicas en obras polif\u00f3nicas y la clasificaci\u00f3n autom\u00e1tica de g\u00e9neros musicales. Entre las posibles aplicaciones de estas t\u00e9cnicas est\u00e1 la catalogaci\u00f3n, indexaci\u00f3n y recuperaci\u00f3n autom\u00e1tica de obras musicales, basadas en su contenido, de grandes bases de datos que contienen obras en formato simb\u00f3lico (partituras digitales, archivos midi, etc.). Otras aplicaciones, en el \u00e1mbito de la musicolog\u00eda computacional, incluyen la caracterizaci\u00f3n de g\u00e9neros musicales y melod\u00edas mediante el an\u00e1lisis autom\u00e1tico del contenido de grandes vol\u00famenes de obras musicales.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>A statistical pattern recognition approach to symbolic music classification<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 A statistical pattern recognition approach to symbolic music classification <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Pedro Jos\u00e9 Ponce De Le\u00f3n Amador <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Alicante<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 26\/09\/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>Jos\u00e9 Manuel I\u00f1esta Quereda<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: francesc josep Ferri rabasa <\/li>\n<li>Carlos augusto Ag\u00f3n amado (vocal)<\/li>\n<li>Rafael Ramirez melendez (vocal)<\/li>\n<li>nicola Orio (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Pedro Jos\u00e9 Ponce De Le\u00f3n Amador El trabajo se enmarca en el dominio de recuperaci\u00f3n de m\u00fasica [&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 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