On the application of generic summarization algorithms to music

Detalhes bibliográficos
Autor(a) principal: Raposo, F.
Data de Publicação: 2015
Outros Autores: Ribeiro, R., de Matos, D. M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/9338
Resumo: Several generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply these algorithms to music. To evaluate their performance, we adopt an extrinsic approach: we compare a Fado genre classifier's performance using truncated contiguous clips against the summaries extracted with those algorithms on two different datasets. We show that Maximal Marginal Relevance (MMR), LexRank, and Latent Semantic Analysis (LSA) all improve classification performance in both datasets used for testing.
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spelling On the application of generic summarization algorithms to musicAutomatic music summarizationGeneric summarization algorithmsSeveral generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply these algorithms to music. To evaluate their performance, we adopt an extrinsic approach: we compare a Fado genre classifier's performance using truncated contiguous clips against the summaries extracted with those algorithms on two different datasets. We show that Maximal Marginal Relevance (MMR), LexRank, and Latent Semantic Analysis (LSA) all improve classification performance in both datasets used for testing.IEEE2015-07-17T13:58:12Z2015-01-01T00:00:00Z20152019-05-03T17:15:18Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/9338eng1070-990810.1109/LSP.2014.2347582Raposo, F.Ribeiro, R.de Matos, D. M.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:51:21Zoai:repositorio.iscte-iul.pt:10071/9338Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:25:25.642602Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv On the application of generic summarization algorithms to music
title On the application of generic summarization algorithms to music
spellingShingle On the application of generic summarization algorithms to music
Raposo, F.
Automatic music summarization
Generic summarization algorithms
title_short On the application of generic summarization algorithms to music
title_full On the application of generic summarization algorithms to music
title_fullStr On the application of generic summarization algorithms to music
title_full_unstemmed On the application of generic summarization algorithms to music
title_sort On the application of generic summarization algorithms to music
author Raposo, F.
author_facet Raposo, F.
Ribeiro, R.
de Matos, D. M.
author_role author
author2 Ribeiro, R.
de Matos, D. M.
author2_role author
author
dc.contributor.author.fl_str_mv Raposo, F.
Ribeiro, R.
de Matos, D. M.
dc.subject.por.fl_str_mv Automatic music summarization
Generic summarization algorithms
topic Automatic music summarization
Generic summarization algorithms
description Several generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply these algorithms to music. To evaluate their performance, we adopt an extrinsic approach: we compare a Fado genre classifier's performance using truncated contiguous clips against the summaries extracted with those algorithms on two different datasets. We show that Maximal Marginal Relevance (MMR), LexRank, and Latent Semantic Analysis (LSA) all improve classification performance in both datasets used for testing.
publishDate 2015
dc.date.none.fl_str_mv 2015-07-17T13:58:12Z
2015-01-01T00:00:00Z
2015
2019-05-03T17:15:18Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10071/9338
url http://hdl.handle.net/10071/9338
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1070-9908
10.1109/LSP.2014.2347582
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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