Musicaiz: a python library for symbolic music generation, analysis and visualization
Hernandez-Olivan, Carlos (Universidad de Zaragoza) ; Beltran, Jose R. (Universidad de Zaragoza)
Resumen: In this article, we present musicaiz, an object-oriented library for analyzing, generating and evaluating symbolic music. The submodules of the package allow the user to create symbolic music data from scratch, build algorithms to analyze symbolic music, encode MIDI data as tokens to train deep learning sequence models, modify existing music data and evaluate music generation systems. The evaluation submodule builds on previous work to objectively measure music generation systems and to be able to reproduce the results of music generation models. The library is publicly available online. We encourage the community to contribute and provide feedback.
Idioma: Inglés
DOI: 10.1016/j.softx.2023.101365
Año: 2023
Publicado en: SoftwareX 22 (2023), 101365 [6 pp]
ISSN: 2352-7110
Factor impacto JCR: 2.4 (2023)
Categ. JCR: COMPUTER SCIENCE, SOFTWARE ENGINEERING rank: 54 / 132 = 0.409 (2023) - Q2 - T2
Factor impacto CITESCORE: 5.5 - Computer Science Applications (Q2) - Software (Q2)
Factor impacto SCIMAGO: 0.544 - Computer Science Applications (Q2) - Software (Q3)
Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T60-20R-AFFECTIVE LAB
Financiación: info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/RTI2018-096986-B-C31
Tipo y forma: Article (Published version)
Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material.
Exportado de SIDERAL (2024-11-22-12:12:20)
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Idioma: Inglés
DOI: 10.1016/j.softx.2023.101365
Año: 2023
Publicado en: SoftwareX 22 (2023), 101365 [6 pp]
ISSN: 2352-7110
Factor impacto JCR: 2.4 (2023)
Categ. JCR: COMPUTER SCIENCE, SOFTWARE ENGINEERING rank: 54 / 132 = 0.409 (2023) - Q2 - T2
Factor impacto CITESCORE: 5.5 - Computer Science Applications (Q2) - Software (Q2)
Factor impacto SCIMAGO: 0.544 - Computer Science Applications (Q2) - Software (Q3)
Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T60-20R-AFFECTIVE LAB
Financiación: info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/RTI2018-096986-B-C31
Tipo y forma: Article (Published version)
Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material. Exportado de SIDERAL (2024-11-22-12:12:20)
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Record created 2023-03-23, last modified 2024-11-25