2026-09-21T19:24:20Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/127217 2025-03-29 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms The student, Austin Lu, accepted the attached license on 2024-12-10 at 16:41. The student, Austin Lu, submitted this Thesis for approval on 2024-12-10 at 17:00. This Thesis was approved for publication on 2024-12-11 at 16:06. DSpace SAF Submission Ingestion Package generated from Vireo submission #21383 on 2025-03-28 at 14:26:03 application/pdf en text Automating acoustic signal processing experiments and audio machine learning datasets using robots Lu, Austin 2024-12-11 Singer, Andrew C Corey, Ryan M Audio Signal Processing Microphone Arrays Spatial Audio Robotics 3d Printing eng 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. 2024-12 Thesis https://hdl.handle.net/2142/127217 Copyright 2024 Austin Lu Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign M.S. Thesis