TY - JOUR
T1 - Separación de fuentes auditivas para pedagogía musical
AU - Lancheros-Molano, Randy Darrell
AU - Triana-Perez, Juan Sebastián
AU - Castañeda-Chaparro, Juan Felipe
AU - Gutiérrez-Naranjo, Felipe Andrés
AU - del Pilar Rueda-Olarte, Andrea
N1 - Publisher Copyright:
© 2020 Universidad Autonoma de Bucaramanga.
PY - 2021
Y1 - 2021
N2 - Harmonics 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.
AB - Harmonics 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.
KW - Machine learning
KW - Sheet music generation
KW - Sound source separation
KW - Web application
UR - http://www.scopus.com/inward/record.url?scp=85108625447&partnerID=8YFLogxK
U2 - 10.29375/25392115.4151
DO - 10.29375/25392115.4151
M3 - Artículo
AN - SCOPUS:85108625447
SN - 1657-2831
VL - 22
SP - 22
EP - 33
JO - Revista Colombiana de Computacion
JF - Revista Colombiana de Computacion
IS - 1
ER -