Título:
A multimodal approach for percussion music transcription from audio and video
Otros títulos:
Coordinador:
Director:
Compilador:
Autor:
Marenco, Bernardo
Fuentes, Magdalena
Lanzaro, Florencia
Rocamora, Martín
Gómez, Alvaro
Fuentes, Magdalena
Lanzaro, Florencia
Rocamora, Martín
Gómez, Alvaro
Tutor:
Tipo de documento:
Ponencia
Editor:
Palabras clave:
Multimodal signal processing
Machine learning applications
Music transcription
Percussion music
Sound classication
Machine learning applications
Music transcription
Percussion music
Sound classication
Descriptores:
Procesamiento de Señales
Año de publicación:
2015
Contenido:
Resumen:
A multimodal approach for percussion music transcription from audio and video recordings is proposed in this work. It is part of an ongoing research effort for the development of tools for computeraided analysis of Candombe drumming, a popular afro-rooted rhythm from Uruguay. Several signal processing techniques are applied to automatically extract meaningful information from each source. This involves detecting certain relevant objects in the scene from the video stream. The location of events is obtained from the audio signal and this information is used to drive the processing of both modalities. Then, the detected events are classified by combining the information from each source in a feature-level fusion scheme. The experiments conducted yield promising results that show the advantages of the proposed method. Keywords: multimodal signal processing, machine learning applications, music transcription, percussion music, sound classification
Descripción:
Trabajo aceptado y presentado en Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015.
metadata.articulos.dc.description.uri:
Editorial:
EN:
Financiadores:
Citación:
Marenco, B., Fuentes, M., Lanzaro, F., Rocamora, M., Gómez, A. "A Multimodal approach for percussion music transcription from audio and video". Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science, v. 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_12
Citación:
metadata.articulos.cc.license.name:
ISBN:
e-ISBN:
ISSN:
ISMN:
Otros identificadores:
Cobertura geográfica:
Cobertura temporal:
Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Procesamiento de Audio
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| MFLRG15.pdf | — | 2.73 MB | Adobe PDF |