{"id":577,"date":"2016-07-18T15:46:34","date_gmt":"2016-07-18T18:46:34","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=577"},"modified":"2016-07-18T15:46:34","modified_gmt":"2016-07-18T18:46:34","slug":"vol10-no1-art2","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art2\/","title":{"rendered":"An\u00e1lise de Componentes Esparsos: Separa\u00e7\u00e3o Cega de Fontes e Aplica\u00e7\u00f5es"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> An\u00e1lise de Componentes Esparsos: Separa\u00e7\u00e3o Cega de Fontes e Aplica\u00e7\u00f5es<\/p>\n<p><strong>Autores:<\/strong> Haddad, Diego B.; Petraglia, Mariane R.; Batalheiro, Paulo B.; Pires Filho, Jorge C.<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> Em diversas aplica\u00e7\u00f5es de separa\u00e7\u00e3o cega de fontes, a hip\u00f3tese de que o n\u00famero de fontes n\u00e3o supera o n\u00famero de misturas n\u00e3o \u00e9 satisfeita. Neste contexto, as mais conhecidas t\u00e9cnicas de An\u00e1lise de Componentes Independentes em geral n\u00e3o podem ser empregadas e cabe recorrer a outros m\u00e9todos &#8211; entre os quais se destaca a An\u00e1lise de Componentes Esparsos (SCA, do ingl\u00eas An\u00e1lise de Componentes Esparsos). Este tutorial almeja expor os fundamentos das principais abordagens de An\u00e1lise de Componentes Esparsos, para o caso de misturas lineares e instant\u00e2neas, bem como apresentar algumas de suas aplica\u00e7\u00f5es. Ao final, o uso de SCA foi validado numa aplica\u00e7\u00e3o de reconhecimento autom\u00e1tico de instrumentos musicais.<\/p>\n<p><strong>Palavras-chave:<\/strong> An\u00e1lise de componentes esparsos; bancos de filtros; transformadas; separa\u00e7\u00e3o cega de sinais<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 17<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol10-no1-art2\">10.21528\/lmln-vol10-no1-art2<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol10-no1-art2.pdf\" rel=\"\">vol10-no1-art2.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol10-no1-art2.bib\" rel=\"\">vol10-no1-art2.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: An\u00e1lise de Componentes Esparsos: Separa\u00e7\u00e3o Cega de Fontes e Aplica\u00e7\u00f5es Autores: Haddad, Diego B.; Petraglia, Mariane R.; Batalheiro, Paulo B.; Pires Filho, Jorge C. Resumo: Em diversas aplica\u00e7\u00f5es de separa\u00e7\u00e3o cega de fontes, a hip\u00f3tese de que o n\u00famero <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art2\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":565,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-577","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>An\u00e1lise de Componentes Esparsos: Separa\u00e7\u00e3o Cega de Fontes e Aplica\u00e7\u00f5es - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art2\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"An\u00e1lise de Componentes Esparsos: Separa\u00e7\u00e3o Cega de Fontes e Aplica\u00e7\u00f5es - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: An\u00e1lise de Componentes Esparsos: Separa\u00e7\u00e3o Cega de Fontes e Aplica\u00e7\u00f5es Autores: Haddad, Diego B.; Petraglia, Mariane R.; Batalheiro, Paulo B.; Pires Filho, Jorge C. 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Resumo: Em diversas aplica\u00e7\u00f5es de separa\u00e7\u00e3o cega de fontes, a hip\u00f3tese de que o n\u00famero Read More ...","og_url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art2\/","og_site_name":"Learning and NonLinear Models","twitter_card":"summary_large_image","twitter_misc":{"Est. tempo de leitura":"1 minuto"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art2\/","url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art2\/","name":"An\u00e1lise de Componentes Esparsos: Separa\u00e7\u00e3o Cega de Fontes e Aplica\u00e7\u00f5es - Learning and NonLinear Models","isPartOf":{"@id":"https:\/\/sbia.org.br\/lnlm\/#website"},"datePublished":"2016-07-18T18:46:34+00:00","breadcrumb":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art2\/#breadcrumb"},"inLanguage":"pt-BR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art2\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art2\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Browse issues","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/"},{"@type":"ListItem","position":2,"name":"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 10 &#8211; N\u00famero 1","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/"},{"@type":"ListItem","position":3,"name":"An\u00e1lise de Componentes Esparsos: Separa\u00e7\u00e3o Cega de Fontes e Aplica\u00e7\u00f5es"}]},{"@type":"WebSite","@id":"https:\/\/sbia.org.br\/lnlm\/#website","url":"https:\/\/sbia.org.br\/lnlm\/","name":"Learning and NonLinear Models","description":"","publisher":{"@id":"https:\/\/sbia.org.br\/lnlm\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"pt-BR"},{"@type":"Organization","@id":"https:\/\/sbia.org.br\/lnlm\/#organization","name":"Learning and NonLinear Models","url":"https:\/\/sbia.org.br\/lnlm\/","logo":{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/","url":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","contentUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","width":398,"height":94,"caption":"Learning and NonLinear Models"},"image":{"@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/577","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/comments?post=577"}],"version-history":[{"count":0,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/577\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/565"}],"wp:attachment":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/media?parent=577"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}