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Evolving a Multi-Classifier System for Multi-Pitch Estimation of Piano Music and Beyond: An Application of Cartesian Genetic Programming

2021-03-24Artigo científico Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia
datacite.subject.fosCiências Naturais::Ciências Físicas
datacite.subject.fosEngenharia e Tecnologia::Engenharia Química
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.authorFernández, Francisco
dc.contributor.authorReis, Gustavo
dc.contributor.authorInácio, Tiago
dc.date.accessioned2026-03-11T17:32:20Z
dc.date.available2026-03-11T17:32:20Z
dc.date.issued2021-03-24
dc.description.abstractThis paper presents a new method with a set of desirable properties for multi-pitch estimation of piano recordings. We propose a framework based on a set of classifiers to analyze audio input and to identify piano notes present in a given audio signal. Our system’s classifiers are 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. Two significant improvements are described: the use of a harmonic mask for better fitness values and a data augmentation process for improving the training stage. The proposed approach achieves com-petitive results using F-measure metrics when compared to state-of-the-art algorithms. Then, we go beyond piano and show how it can be directly applied to other musical instruments, achieving even better results. Our system’s architecture is also described to show the feasibility of its parallelization and its implementation as a real-time system. Our methodology is also a white-box optimization approach that allows for clear analysis of the solutions found and for researchers to learn and test improvements based on the new findings.eng
dc.description.sponsorshipFunding This research was funded by the Spanish Ministry of Economy and Competitiveness under project TIN2017-85727-C4-{2,4}-P; by the Regional Government of Extremadura, Department of Commerce and Economy, the European Regional Development Fund, a way to build Europe, under project IB16035; and by Junta de Extremadura, project GR15068 and project GR18049. Acknowledgments The authors would like to thank to P. Domingues for providing powerful hardware at Polytechnic of Leiria to perform important tests; P. Chavez for his hard work on configuring the blade machines at the Universidad de Extremadura, Mérida, so that we could perform tests; and Jorge Alvarado for technical support on installing and configuring software and virtualization solutions. We also would like to thank CIIC—Computer Science and Communication Research Centre - for the logistic support and co-funding by FCT - Fundação para a Ciência e Tecnologia, I.P., under the project UIDB/04524/2020.
dc.identifier.citationMiragaia, R.; Fernández, F.; Reis, G.; Inácio, T. Evolving a Multi-Classifier System for Multi-Pitch Estimation of Piano Music and Beyond: An Application of Cartesian Genetic Programming. Appl. Sci. 2021, 11, 2902. https://doi.org/10.3390/app11072902.
dc.identifier.doi10.3390/app11072902
dc.identifier.eissn2076-3417
dc.identifier.urihttp://hdl.handle.net/10400.8/15846
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationResearch Center in Informatics and Communications
dc.relation.hasversionhttps://www.mdpi.com/2076-3417/11/7/2902
dc.relation.ispartofApplied Sciences
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectmulti-pitch estimation
dc.subjectmultiple-F0 estimation
dc.subjectevolutionary computing
dc.subjectCartesian genetic programming
dc.subjectgenetic programming
dc.subjectmusic transcription
dc.subjectmachine learning
dc.titleEvolving a Multi-Classifier System for Multi-Pitch Estimation of Piano Music and Beyond: An Application of Cartesian Genetic Programmingeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDB/04524/2020
oaire.awardTitleResearch Center in Informatics and Communications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04524%2F2020/PT
oaire.citation.endPage27
oaire.citation.issue7
oaire.citation.startPage1
oaire.citation.titleApplied Sciences (Switzerland)
oaire.citation.volume11
oaire.fundingStream6817 - DCRRNI ID
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
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublicationc3934650-8cbe-40cd-bb29-31c57baa49e2
relation.isAuthorOfPublication77b7fb9b-3584-4057-a3d8-de29d3fab6c1
relation.isAuthorOfPublication.latestForDiscoveryc3934650-8cbe-40cd-bb29-31c57baa49e2
relation.isProjectOfPublication67435020-fe0d-4b46-be85-59ee3c6138c7
relation.isProjectOfPublication.latestForDiscovery67435020-fe0d-4b46-be85-59ee3c6138c7

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This paper presents a new method with a set of desirable properties for multi-pitch estimation of piano recordings. We propose a framework based on a set of classifiers to analyze audio input and to identify piano notes present in a given audio signal. Our system’s classifiers are 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. Two significant improvements are described: the use of a harmonic mask for better fitness values and a data augmentation process for improving the training stage. The proposed approach achieves com-petitive results using F-measure metrics when compared to state-of-the-art algorithms. Then, we go beyond piano and show how it can be directly applied to other musical instruments, achieving even better results. Our system’s architecture is also described to show the feasibility of its parallelization and its implementation as a real-time system. Our methodology is also a white-box optimization approach that allows for clear analysis of the solutions found and for researchers to learn and test improvements based on the new findings.
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