Modeling onset spectral features for discrimination of drum sounds
Resumen:
Motivated by practical problems related to ongoing research on Candombe drumming (a popular afro-rooted rhythm from Uruguay), this paper proposes an approach for recognizing drum sounds in audio signals that models for sound classification the same audio spectral features employed in onset detection. Among the reported experiments involving recordings of real performances, one aims at finding the predominant Candombe drum heard in an audio file, while the other attempts to identify those temporal segments within a performance when a given sound pattern is played. The attained results are promising and suggest many ideas for future research. Keywords: Audio signal processing · Machine learning applications · Musical instrument recognition · Percussion music · Candombe drumming
| 2015 | |
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Audio signal processing Machine learning applications Musical instrument recognition Percussion music Candombe drumming Procesamiento de Señales |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/42683 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| Sumario: | Trabajo presentado en Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015 |
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