{"id":103022,"date":"2018-03-11T10:25:25","date_gmt":"2018-03-11T10:25:25","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/musical-expectation-modelling-from-audio-a-causal-mid-level-approach-to-predictive-representation-and-learning-of-spectro-temporal-events\/"},"modified":"2018-03-11T10:25:25","modified_gmt":"2018-03-11T10:25:25","slug":"musical-expectation-modelling-from-audio-a-causal-mid-level-approach-to-predictive-representation-and-learning-of-spectro-temporal-events","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/inteligencia-artificial\/musical-expectation-modelling-from-audio-a-causal-mid-level-approach-to-predictive-representation-and-learning-of-spectro-temporal-events\/","title":{"rendered":"Musical expectation modelling from audio:  a causal mid-level approach to predictive representation and learning of spectro-temporal events"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Amaury Hazan <\/strong><\/h2>\n<p>Esta tesis presenta un modelo computacional de expectativa musical, que es un aspecto muy importante de como procesamos la m\u00fasica que oimos. Muchos fenomenos relacionados con el procesamiento de la musica est\u00e1n vinculados a una capacidad para anticipar la continuaci\u00f3n de una pieza de m\u00fasica. Nos enfocaremos en un acercamiento estad\u00edstico de la expectativa musical, modelando los procesos de aprendizaje y de predicci\u00f3n de las regularidades espectro-temporales de forma causal. el principio de modelado estad\u00edstico de la expectativa se puede aplicar a varias representaciones de estructuras musicales, desde las notaciones simbolicas a la se\u00f1ales de audio. Primero demostramos que ciertos algoritmos de aprendizaje de secuencias se pueden usar y evaluar en el contexto de la percepci\u00f3n y el aprendizaje de secuencias auditivas. Luego, proponemos una representaci\u00f3n, denominada que\/cuando, para representar eventos musicales de una forma que permite describir y aprender la estructura secuencial de unidades ac\u00fasticas en se\u00f1ales de audio musical. aplicamos esta representaci\u00f3n para describir y anticipar caracteristicas timbricas y ritmos. Sugerimos que se pueden explotar las propiedades del modelo de expectativa para resolver tareas de an\u00e1lisis como la segmentaci\u00f3n estructural de piezas musicales. Finalmente, exploramos las implicaciones de nuestro modelo a la hora de definir nuevas aplicaciones en el contexto de la transcripci\u00f3n en tiempo real, la sintesis concatenativa y la visualizaci\u00f3n.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Musical expectation modelling from audio:  a causal mid-level approach to predictive representation and learning of spectro-temporal events<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Musical expectation modelling from audio:  a causal mid-level approach to predictive representation and learning of spectro-temporal events <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Amaury Hazan <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Pompeu fabra<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 16\/07\/2010<\/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: Jos\u00e9 manuel I\u00f1esta quereda <\/li>\n<li>g\u00e9rard Assayag (vocal)<\/li>\n<li>josep lLuis Arcos rosell (vocal)<\/li>\n<li>fabien Gouyon (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Amaury Hazan Esta tesis presenta un modelo computacional de expectativa musical, que es un aspecto muy importante [&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":[2528,9132,18712,4030,11714],"tags":[208758,166888,208173,11898,60350,38388],"class_list":["post-103022","post","type-post","status-publish","format-standard","hentry","category-inteligencia-artificial","category-musica-y-musicologia","category-pompeu-fabra","category-tecnicas-de-prediccion-estadistica","category-teoria-de-la-representacion","tag-amaury-hazan","tag-fabien-gouyon","tag-gerard-assayag","tag-jose-manuel-inesta-quereda","tag-josep-lluis-arcos-rosell","tag-xavier-serra-casals"],"_links":{"self":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/103022","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=103022"}],"version-history":[{"count":0,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/103022\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/media?parent=103022"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/categories?post=103022"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/tags?post=103022"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}