Comparing audio descriptors for singing voice detection in music audio files
Resumen:
Given the relevance of the singing voice in popular western music, a system able to reliable identify those portions of a music audio file containing vocals would be very useful. In this work, we explore already used descriptors to perform this task and compare the performance of a statistical classifier using each kind of them, concluding that MFCC are the most appropriate. As an outcome of our study, an effective statistical classification system with a reduced set of descriptors for singing voice detection in music audio files is presented. The performance of the system is validated using independent datasets of popular music for training, validation and testing, reaching a classification performance of 78.5% on the testing set.
| 2007 | |
| Procesamiento de Señales | |
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/38794 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| Sumario: | Given the relevance of the singing voice in popular western music, a system able to reliable identify those portions of a music audio file containing vocals would be very useful. In this work, we explore already used descriptors to perform this task and compare the performance of a statistical classifier using each kind of them, concluding that MFCC are the most appropriate. As an outcome of our study, an effective statistical classification system with a reduced set of descriptors for singing voice detection in music audio files is presented. The performance of the system is validated using independent datasets of popular music for training, validation and testing, reaching a classification performance of 78.5% on the testing set. |
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