Separación de fuentes auditivas para pedagogía musical

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dc.contributor.authorLancheros Molano, Randy Darrell
dc.contributor.authorTriana Perez, Juan Sebastián
dc.contributor.authorCastañeda Chaparro, Juan Felipe
dc.contributor.authorGutiérrez Naranjo, Felipe Andrés
dc.contributor.authorRueda Olarte, Andrea del Pilar
dc.date.accessioned2024-09-10T20:47:37Z
dc.date.available2024-09-10T20:47:37Z
dc.date.issued2021-04-15
dc.description.abstractHarmonics espera apoyar a la pedagogía musical, ofreciendo un producto concreto con el cual los interesados en aprender a tocar un instrumento puedan practicar. Se entrenó un modelo para identificar y aislar las pistas singulares de una canción, por medio de TensorFlow y herramientas para realizar la separación de fuentes auditivas y producir partituras genuinas, basadas en un algoritmo de transcripción musical (para pianos, bajos, batería y voz, específicamente), que los principiantes puedan visualizar, editar y descargar (en formatos .PDF y .MIDI), ajustándose a su ritmo de práctica. Se consideraron tres métodos de separación de fuentes, bajo las siguientes restricciones: emplear una única canción como archivo de entrada, que ésta fuera moderadamente compleja (compuesta por un conjunto de entre tres y seis instrumentos) y que la cantidad de muestras –canciones compuestas por instrumentos relevantes y pistas de cada instrumento por separado– aptas para el entrenamiento del modelo, sean sumamente escasas.spa
dc.description.abstractenglishHarmonics hopes to support musical pedagogy, offering a concrete product with which those interested in learning to play an instrument can practice. We trained a model to identify and isolate the singular tracks of a song through TensorFlow and tools to make the separation of auditory sources and produce genuine sheet music, based on a musical transcription algorithm (specifically for pianos, basses, drums, and voice) that beginners can visualize, edit, and download (in .PDF and .MIDI formats), adjusting at their own pace. Three methods of source separation were considered, under the following restrictions: Use a single song as an input file, which it was moderately complex (composed of a set of between three and six instruments), and that the number of samples -songs composed by relevant instruments and tracks of each standalone instrument - suitable for model training, would be extremely scarce.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.29375/25392115.4151
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga UNABspa
dc.identifier.issnISSN: 1657-2831spa
dc.identifier.issne-ISSN: 2539-2115spa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.identifier.urihttp://hdl.handle.net/20.500.12749/26460
dc.language.isospaspa
dc.publisherUniversidad Autónoma de Bucaramanga UNABspa
dc.relationhttps://revistas.unab.edu.co/index.php/rcc/article/view/4151/3404spa
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dc.relation.urihttps://revistas.unab.edu.co/index.php/rcc/issue/view/273spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.sourceVol. 22 Núm. 1 (2021): Revista Colombiana de Computación (Enero-Junio); 22-33spa
dc.subjectAprendizaje de máquinaspa
dc.subjectSeparación de fuentes auditivasspa
dc.subjectGeneración de partiturasspa
dc.subjectAplicación webspa
dc.subject.keywordsMachine learningeng
dc.subject.keywordsSound source separationeng
dc.subject.keywordsSheet music generationeng
dc.subject.keywordsWeb applicationeng
dc.titleSeparación de fuentes auditivas para pedagogía musicalspa
dc.title.translatedSound source separation for musical pedagogyeng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.type.localArtículospa
dc.type.redcolhttp://purl.org/redcol/resource_type/ART

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