Name
Abstract | ||
|---|---|---|
This paper addresses the issue of drum sound classification in the context of automatic rhythm modification of drum loops. The proposed method segments the signal using an onset detection algorithm, characterises segmented sounds using a spectral feature set, and classifies them using k-means clustering. We propose a simple taxonomy for the grouping of different instrumental sounds under a few utilitarian labels. Results demonstrate the adequacy of our proposed taxonomy while showing that our classification approach outperforms commonly-used supervised learning techniques |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICASSP.2006.1661255 | Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference |
Keywords | Field | DocType |
acoustic signal detection,acoustic signal processing,musical instruments,signal classification,automatic rhythm modification,drum loops,drum sound analysis,drum sound classification,k-means clustering,onset detection algorithm,rhythm manipulation,segmented sounds | k-means clustering,Signal processing,Pattern recognition,Computer science,Support vector machine,Drum,Supervised learning,Speech recognition,Artificial intelligence,Cluster analysis,Hidden Markov model,Rhythm | Conference |
Volume | ISSN | ISBN |
5 | 1520-6149 | 1-4244-0469-X |
Citations | PageRank | References |
3 | 0.53 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Pablo Bello | 1 | 3 | 0.53 |
| Emmanuel Ravelli | 2 | 38 | 4.70 |
| Sandler, Mark B. | 3 | 3 | 0.53 |