Withdraw
Loading…
Automating acoustic signal processing experiments and audio machine learning datasets using robots
Lu, Austin
Loading…
Permalink
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
- 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.
- Graduation Semester
- 2024-12
- Type of Resource
- Thesis
- Handle URL
- https://hdl.handle.net/2142/127217
- Copyright and License Information
- Copyright 2024 Austin Lu
Owning Collections
Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at IllinoisManage Files
Loading…
Edit Collection Membership
Loading…
Edit Metadata
Loading…
Edit Properties
Loading…
Embargoes
Loading…