Logo do repositório
 
Publicação

Evolving a multi-classifier system with cartesian genetic programming for multi-pitch estimation of polyphonic piano music

2021-04-22Artigo científico Acesso aberto
Ver registo simples
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg10:Reduzir as Desigualdades
dc.contributor.authorMiragaia, Rolando
dc.contributor.authorVega, Francisco Fernandez de
dc.contributor.authorReis, Gustavo
dc.date.accessioned2026-03-13T15:34:08Z
dc.date.available2026-03-13T15:34:08Z
dc.date.issued2021-04-22
dc.descriptionConference date - March 22 - 26, 2021
dc.description.abstractThis paper presents a new method for multi-pitch estimation on piano recordings. We propose a framework based on a set of classifiers to analyze the audio input and identify the piano notes present on the given audio signal. Our system's classifiers were evolved using Cartesian Genetic Programming: we take advantage of Cartesian Genetic Programming to evolve a set of mathematical functions that act as independent classifiers for piano notes. Our latest improvements are also presented, including test results using F-measure metrics. Our system architecture is also described to show the feasibility of its parallelization and implementation as a real time system. The proposed approach achieved competitive results, when compared to the state of the art.eng
dc.description.sponsorshipWe acknowledge support from Spanish Ministry of Economy and Competitiveness under project TIN2017-85727-C4-{2,4}-P, Regional Government of Extremadura, Department of Commerce and Economy, the European Regional Development Fund, a way to build Europe, under the project IB16035 and Junta de Extremadura, project GR15068 and project GR18049.
dc.identifier.citationRolando Miragaia, Francisco Fernandez de Vega, and Gustavo Reis. 2021. Evolving a multi-classifier system with cartesian genetic programming for multi-pitch estimation of polyphonic piano music. In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC '21). Association for Computing Machinery, New York, NY, USA, 472–480. https://doi.org/10.1145/3412841.3441927.
dc.identifier.doi10.1145/3412841.3441927
dc.identifier.isbn9781450381048
dc.identifier.urihttp://hdl.handle.net/10400.8/15866
dc.language.isoeng
dc.peerreviewedyes
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.hasversionhttps://dl.acm.org/doi/10.1145/3412841.3441927
dc.relation.ispartofProceedings of the 36th Annual ACM Symposium on Applied Computing
dc.rights.uriN/A
dc.subjectGenetic Programming
dc.subjectMulti pitch Estimation
dc.subjectAutomatic piano music transcription
dc.subjectpiano notes detection
dc.titleEvolving a multi-classifier system with cartesian genetic programming for multi-pitch estimation of polyphonic piano musiceng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferenceDate2021-03
oaire.citation.conferencePlaceRepublic of Korea
oaire.citation.endPage480
oaire.citation.startPage472
oaire.citation.titleProceedings of the ACM Symposium on Applied Computing
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMiragaia
person.familyNameJorge dos Reis
person.givenNameRolando
person.givenNameGustavo Miguel
person.identifier.ciencia-idC712-E02E-0ED2
person.identifier.ciencia-idC41A-BC63-08E6
person.identifier.orcid0000-0003-4213-9302
person.identifier.orcid0000-0002-5903-8754
person.identifier.ridGLS-3615-2022
person.identifier.scopus-author-id26422369700
relation.isAuthorOfPublicationc3934650-8cbe-40cd-bb29-31c57baa49e2
relation.isAuthorOfPublication77b7fb9b-3584-4057-a3d8-de29d3fab6c1
relation.isAuthorOfPublication.latestForDiscoveryc3934650-8cbe-40cd-bb29-31c57baa49e2

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
Evolving a multi-classifier system with cartesian genetic programming for multi-pitch estimation of polyphonic piano music.pdf
Tamanho:
1.57 MB
Formato:
Adobe Portable Document Format
Descrição:
This paper presents a new method for multi-pitch estimation on piano recordings. We propose a framework based on a set of classifiers to analyze the audio input and identify the piano notes present on the given audio signal. Our system's classifiers were evolved using Cartesian Genetic Programming: we take advantage of Cartesian Genetic Programming to evolve a set of mathematical functions that act as independent classifiers for piano notes. Our latest improvements are also presented, including test results using F-measure metrics. Our system architecture is also described to show the feasibility of its parallelization and implementation as a real time system. The proposed approach achieved competitive results, when compared to the state of the art.
Ver/Abrir
Licença
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
license.txt
Tamanho:
1.32 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Ver/Abrir

Coleções

CIIC - Artigos em Revistas com Peer Review
ESTG - Artigos em revistas internacionais