Automating acoustic signal processing experiments and audio machine learning datasets using robots
Lu, Austin
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https://hdl.handle.net/2142/127217
Description
Title
Automating acoustic signal processing experiments and audio machine learning datasets using robots
Author(s)
Lu, Austin
Issue Date
2024-12-11
Director of Research (if dissertation) or Advisor (if thesis)
Singer, Andrew C
Corey, Ryan M
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Audio Signal Processing
Microphone Arrays
Spatial Audio
Robotics
3d Printing
Language
eng
Abstract
We develop specialized robots for audio and acoustic experiments, which in turn facilitate acoustic signal processing and audio machine learning. Our robo-acoustic mannequin, a low-cost 3D printed device, is custom-made to enable interesting spatially-dynamic experiments. We explore simple solutions to quiet actuation, thus avoiding the infamous problem of audible robot noise, and we open-source our design to stimulate further development. Using this new research resource, we empirically study how motion, specifically head-turning, affects the objective performance of a spatially-adaptive MVDR beamformer. We find the surprising result that the presence of motion has a significant effect, but the rate of motion does not. To study more intricate scenarios, we also design a multi-robot mechatronic recording studio that automatically captures high-resolution labeled audio datasets. Large-scale multiple-day-spanning recordings that would be impossible to do manually become possible through this work. The multi-robot system is accessed by geographically disparate researchers via a “DAW for robots” web interface, thus demonstrating the potential of shared, collaborative audio-robot workspaces. Overall, we expect our work to motivate new and interesting robot-enhanced audio experiments and datasets.
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