Title
WaveTune: Millimeter Wave Radar based Hand Gesture Recognition for Musical Applications
Author
Juneja, Suchdeep Singh (TU Delft Electrical Engineering, Mathematics and Computer Science)
Contributor
Zuniga, Marco (mentor) 
Wang, Q. (graduation committee)
Vaidya, G. (graduation committee)
Degree granting institution
Delft University of Technology
Programme
Electrical Engineering | Embedded Systems
Date
2023-08-30
Abstract
In the ever-evolving field of music technology, new solutions continue to emerge that enhance musical expression and creativity. This thesis introduces WaveTune, a novel lightweight hand gesture recognition system that enables real-time control of musical composition and performance through natural hand motions.
WaveTune utilizes Millimeter Wave radar technology to capture gesture data, combined with optimized deep learning techniques for real-time recognition. This provides an accessible and non-intrusive platform for gesture control that enhances privacy since no visual data is recorded. Users can dynamically select tracks and control musical parameters in real-time using expressive hand motions, integrating seamlessly with music software.
A key innovation of WaveTune is the development of an optimized gesture recognition model that achieves high accuracy for real-time music interaction while minimizing complexity. This is accomplished through novel optimizations to a state-of-the-art point cloud classification architecture, resulting in an efficient and tailored model for fluid musical control.
Furthermore, WaveTune promotes open-source collaboration by providing full access to code, configurations and datasets, inviting the community to build upon this system.
Subject
mmWave
Hand Gesture Recognition
Music technology
Machine Learning (ML)
FMCW Radar
Music
To reference this document use:
http://resolver.tudelft.nl/uuid:8058f786-c737-4143-887a-ca97decd4a5f
Embargo date
2025-09-01
Part of collection
Student theses
Document type
master thesis
Rights
© 2023 Suchdeep Singh Juneja