Realtime Classification of Hand-Drum Strokes
- Michael Krzyzaniak, Arizona State University School of Arts, Media + Engineering
- Garth Paine, Arizona State University School of Arts, Media + Engineering
- 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 affixed 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 website.
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