Studying the Timing of Discrete Musical Air Gestures

Author:

Dahl Luke1

Affiliation:

1. Composition and Computer Technologies McIntire Department of Music University of Virginia P.O. Box 400176, Charlottesville, Virginia 22904, USA

Abstract

Motion-sensing technologies enable musical interfaces where a performer controls sound by moving his or her body “in the air,” without touching a physical object. These interfaces work well when the movement and resulting sound are smooth and continuous, but it has proven difficult to design air instruments that trigger discrete sounds with precision that feels natural to performers and allows them to play rhythmically complex music. This article presents a study of “air drumming” gestures. Participants performed drumming-like gestures in time to simple recorded rhythms. These movements were recorded and examined to look for aspects of the movement that correspond to the timing of the sounds. The goal is to understand what we do with our bodies when we gesture in the air to trigger a sound. Two movement features of the hand are studied: Hits are the moment where the hand changes direction at the end of the striking gesture, and acceleration peaks are sharp peaks in magnitude acceleration as the hand decelerates. Hits and acceleration peaks are also detected for the movement of the wrist. It is found that the acceleration peaks are more useful than the hits because they occur earlier and with less variability, and their timing changes less with note speed. It is also shown that timing differences between hand and wrist features can be used to group performers into different movement styles.

Publisher

MIT Press - Journals

Subject

Computer Science Applications,Music,Media Technology

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Efficient Real-Time Air Drumming Approach Using MediaPipe Hand Gesture Model;2023 5th International Conference on Advancements in Computing (ICAC);2023-12-07

2. Air Drums, and Bass: Anticipating Musical Gestures in Accelerometer Signals with a Lightweight CNN;2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP);2023-09-17

3. Method Development for Multimodal Data Corpus Analysis of Expressive Instrumental Music Performance;Frontiers in Psychology;2020-12-04

4. Creating Space for Facilitated Music Performance;Proceedings of the 12th International Audio Mostly Conference on Augmented and Participatory Sound and Music Experiences;2017-08-23

5. Comparing the Timing of Movement Events for Air-Drumming Gestures;Music, Mind, and Embodiment;2016

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