Abstract
Herein is presented a method of classifying hand-drum strokes in real-time by analyzing 50 milliseconds of audio signal as recorded by a contact-mic a xed to the body of the instrument. The classifier performs with an average accuracy of about 95% across several experiments on archetypical strokes, and 89% on uncontrived playing. A complete ANSI C implementation for OSX and Linux is available on the author’s website1 .
| Original language | English (US) |
|---|---|
| Pages (from-to) | 400-403 |
| Number of pages | 4 |
| Journal | Proceedings of the International Conference on New Interfaces for Musical Expression |
| State | Published - 2015 |
| Event | 15th International conference on New Interfaces for Musical Expression, NIME 2015 - Baton Rouge, United States Duration: May 31 2015 → Jun 3 2015 |
Keywords
- Bongos
- Cajon
- Classification
- Darbuka
- Djembe
- Doumbek
- Drum
- Frame drum
- HRI
- Kiki
- Machine learning
- NIME
- Percussion
- Signal processing
- Stroke
- Timbre
ASJC Scopus subject areas
- Control and Systems Engineering
- Signal Processing
- Instrumentation
- Music
- Human-Computer Interaction
- Hardware and Architecture
- Computer Science Applications