A multimodal approach for percussion music transcription from audio and video
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
| 2015 | |
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Multimodal signal processing Machine learning applications Music transcription Percussion music Sound classication Procesamiento de Señales |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/42666 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522940409872384 |
|---|---|
| author | Marenco, Bernardo |
| author2 | Fuentes, Magdalena Lanzaro, Florencia Rocamora, Martín Gómez, Alvaro |
| author2_role | author author author author |
| author_facet | Marenco, Bernardo Fuentes, Magdalena Lanzaro, Florencia Rocamora, Martín Gómez, Alvaro |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Marenco, Bernardo Fuentes, Magdalena Lanzaro, Florencia Rocamora, Martín Gómez, Alvaro |
| dc.date.accessioned.none.fl_str_mv | 2024-02-26T19:52:32Z |
| dc.date.available.none.fl_str_mv | 2024-02-26T19:52:32Z |
| dc.date.issued.es.fl_str_mv | 2015 |
| dc.date.submitted.es.fl_str_mv | 20240223 |
| dc.description.abstract.none.fl_txt_mv | 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 |
| dc.description.es.fl_txt_mv | Trabajo aceptado y presentado en Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. |
| dc.identifier.citation.es.fl_str_mv | 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 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/42666 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Multimodal signal processing Machine learning applications Music transcription Percussion music Sound classication |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | A multimodal approach for percussion music transcription from audio and video |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Trabajo aceptado y presentado en Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_72c8588499c1f622895afe086ce095f2 |
| identifier_str_mv | 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 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/42666 |
| publishDate | 2015 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2024-02-26T19:52:32Z2024-02-26T19:52:32Z201520240223Marenco, 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_12https://hdl.handle.net/20.500.12008/42666Trabajo aceptado y presentado en Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015.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 classificationMade available in DSpace on 2024-02-26T19:52:32Z (GMT). No. of bitstreams: 5 MFLRG15.pdf: 2859315 bytes, checksum: 6a1718f041d332e52d03f7d963890819 (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4244 bytes, checksum: 528b6a3c8c7d0c6e28129d576e989607 (MD5) Previous issue date: 2015enengLas obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Multimodal signal processingMachine learning applicationsMusic transcriptionPercussion musicSound classicationProcesamiento de SeñalesA multimodal approach for percussion music transcription from audio and videoPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaMarenco, BernardoFuentes, MagdalenaLanzaro, FlorenciaRocamora, MartínGómez, AlvaroProcesamiento de SeñalesProcesamiento de 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- Universidad de la Repúblicafalse |
| spellingShingle | A multimodal approach for percussion music transcription from audio and video Marenco, Bernardo Multimodal signal processing Machine learning applications Music transcription Percussion music Sound classication Procesamiento de Señales |
| status_str | publishedVersion |
| title | A multimodal approach for percussion music transcription from audio and video |
| title_full | A multimodal approach for percussion music transcription from audio and video |
| title_fullStr | A multimodal approach for percussion music transcription from audio and video |
| title_full_unstemmed | A multimodal approach for percussion music transcription from audio and video |
| title_short | A multimodal approach for percussion music transcription from audio and video |
| title_sort | A multimodal approach for percussion music transcription from audio and video |
| topic | Multimodal signal processing Machine learning applications Music transcription Percussion music Sound classication Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/42666 |
