Name
Abstract | ||
|---|---|---|
Automatic classification of musical instruments is an important task for music transcription as well as for professionals such as audio designers, engineers and musicians. Unfortunately, only a limited amount of effort has been conducted to automatically classify percussion instrument in the last years. The studies that deal with percussion sounds are usually restricted to distinguish among the instruments in the drum kit such as toms vs. snare drum vs. bass drum vs. cymbals. In this paper, we are interested in a more challenging task of discriminating sounds produced by the same percussion instrument. Specifically, sounds from different drums cymbals types. Cymbals are known to have indefinite pitch, nonlinear and chaotic behavior. We also identify how the sound of a specific cymbal was produced (e.g., roll or choke movements performed by a drummer). We achieve an accuracy of 96.59% for cymbal type classification and 91.54% in a classification problem with 12 classes which represent the cymbal type and the manner or region that the cymbals are struck. Both results were obtained with Support Vector Machine algorithm using the Line Spectral Frequencies as audio descriptor. We believe that our results can be useful for a more detailed automatic drum transcription and for other related applications as well for audio professionals. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/IJCNN.2015.7280342 | 2015 International Joint Conference on Neural Networks (IJCNN) |
Keywords | Field | DocType |
automatic drum sound classification,automatic musical instrument classification,music transcription,drums cymbal type,cymbal type classification,support vector machine algorithm,line spectral frequency,audio descriptor | Mel-frequency cepstrum,Percussion,Computer science,Support vector machine,Drum,Speech recognition,Support vector machine algorithm | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vinícius M. A. de Souza | 1 | 33 | 6.14 |
| Gustavo E. Batista | 2 | 1928 | 92.83 |
| Nilson E. Souza-Filho | 3 | 0 | 0.34 |