A Prototype for Classification of Classical Music Using Neural Networks
Autor(a) principal: | |
---|---|
Data de Publicação: | 2004 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.ismt.pt/handle/123456789/32 |
Resumo: | As a result of recent technological innovations, there has been a tremendous growth in the Electronic Music Distribution industry. In this way, tasks such us automatic music genre classification address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. As for feature extraction, we use features such as the number of zero crossings, loudness, spectral centroid, bandwidth and uniformity. These are statistically manipulated, making a total of 40 features. As for the task of genre modeling, we train a feedforward neural network (FFNN). A taxonomy of subgenres of classical music is used. We consider three classification problems: in the first one, we aim at discriminating between music for flute, piano and violin; in the second problem, we distinguish choral music from opera; finally, in the third one, we aim at discriminating between all five genres. Preliminary results are presented and discussed, which show that the presented methodology may be a good starting point for addressing more challenging tasks, such as using a broader range of musical categories. |
id |
RCAP_dd3fb006035b8033e41173c3e070e29b |
---|---|
oai_identifier_str |
oai:repositorio.ismt.pt:123456789/32 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
A Prototype for Classification of Classical Music Using Neural NetworksRedes neurais - Neural networksClassifica??o musical - Music classificationProcessamento de sinal de m?sica - Music signal processingRecupera??o de informa??es de m?sica - Music information retrievalAs a result of recent technological innovations, there has been a tremendous growth in the Electronic Music Distribution industry. In this way, tasks such us automatic music genre classification address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. As for feature extraction, we use features such as the number of zero crossings, loudness, spectral centroid, bandwidth and uniformity. These are statistically manipulated, making a total of 40 features. As for the task of genre modeling, we train a feedforward neural network (FFNN). A taxonomy of subgenres of classical music is used. We consider three classification problems: in the first one, we aim at discriminating between music for flute, piano and violin; in the second problem, we distinguish choral music from opera; finally, in the third one, we aim at discriminating between all five genres. Preliminary results are presented and discussed, which show that the presented methodology may be a good starting point for addressing more challenging tasks, such as using a broader range of musical categories.Proceedings of the Eighth IASTED International Conference2013-01-16T13:19:26Z2013-01-162004-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectapplication/pdfhttp://repositorio.ismt.pt/handle/123456789/32enghttp://repositorio.ismt.pt/handle/123456789/32Malheiro, RicardoPaiva, Rui PedroMendes, A. J.Mendes, T.Cardoso, A.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:RCAAP2023-12-15T14:57:45Zoai:repositorio.ismt.pt:123456789/32Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:53:39.177318Repositório 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 |
A Prototype for Classification of Classical Music Using Neural Networks |
title |
A Prototype for Classification of Classical Music Using Neural Networks |
spellingShingle |
A Prototype for Classification of Classical Music Using Neural Networks Malheiro, Ricardo Redes neurais - Neural networks Classifica??o musical - Music classification Processamento de sinal de m?sica - Music signal processing Recupera??o de informa??es de m?sica - Music information retrieval |
title_short |
A Prototype for Classification of Classical Music Using Neural Networks |
title_full |
A Prototype for Classification of Classical Music Using Neural Networks |
title_fullStr |
A Prototype for Classification of Classical Music Using Neural Networks |
title_full_unstemmed |
A Prototype for Classification of Classical Music Using Neural Networks |
title_sort |
A Prototype for Classification of Classical Music Using Neural Networks |
author |
Malheiro, Ricardo |
author_facet |
Malheiro, Ricardo Paiva, Rui Pedro Mendes, A. J. Mendes, T. Cardoso, A. |
author_role |
author |
author2 |
Paiva, Rui Pedro Mendes, A. J. Mendes, T. Cardoso, A. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Malheiro, Ricardo Paiva, Rui Pedro Mendes, A. J. Mendes, T. Cardoso, A. |
dc.subject.por.fl_str_mv |
Redes neurais - Neural networks Classifica??o musical - Music classification Processamento de sinal de m?sica - Music signal processing Recupera??o de informa??es de m?sica - Music information retrieval |
topic |
Redes neurais - Neural networks Classifica??o musical - Music classification Processamento de sinal de m?sica - Music signal processing Recupera??o de informa??es de m?sica - Music information retrieval |
description |
As a result of recent technological innovations, there has been a tremendous growth in the Electronic Music Distribution industry. In this way, tasks such us automatic music genre classification address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. As for feature extraction, we use features such as the number of zero crossings, loudness, spectral centroid, bandwidth and uniformity. These are statistically manipulated, making a total of 40 features. As for the task of genre modeling, we train a feedforward neural network (FFNN). A taxonomy of subgenres of classical music is used. We consider three classification problems: in the first one, we aim at discriminating between music for flute, piano and violin; in the second problem, we distinguish choral music from opera; finally, in the third one, we aim at discriminating between all five genres. Preliminary results are presented and discussed, which show that the presented methodology may be a good starting point for addressing more challenging tasks, such as using a broader range of musical categories. |
publishDate |
2004 |
dc.date.none.fl_str_mv |
2004-09-01T00:00:00Z 2013-01-16T13:19:26Z 2013-01-16 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ismt.pt/handle/123456789/32 |
url |
http://repositorio.ismt.pt/handle/123456789/32 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://repositorio.ismt.pt/handle/123456789/32 |
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 |
Proceedings of the Eighth IASTED International Conference |
publisher.none.fl_str_mv |
Proceedings of the Eighth IASTED International Conference |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799136424635662336 |