| Paper ID | AUD-17.3 |
| Paper Title |
PREDICTION OF OBJECT GEOMETRY FROM ACOUSTIC SCATTERING USING CONVOLUTIONAL NEURAL NETWORKS |
| Authors |
Ziqi Fan, University of Florida, United States; Vibhav Vineet, Microsoft Research, United States; Chenshen Lu, University of Florida, United States; T.W. Wu, University of Kentucky, United States; Kyla McMullen, University of Florida, United States |
| Session | AUD-17: Modeling, Analysis and Synthesis of Acoustic Environments 3: Acoustic Analysis |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-MAAE] Modeling, Analysis and Synthesis of Acoustic Environments |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Acoustic scattering is strongly influenced by boundary geometry of objects over which sound scatters. The present work proposes a method to infer object geometry from scattering features by training convolutional neural networks. The training data is generated from a fast numerical solver developed on CUDA. The complete set of simulations is sampled to generate multiple datasets containing different amounts of channels and diverse image resolutions. The robustness of our approach in response to data degradation is evaluated by comparing the performance of networks trained using the datasets with varying levels of data degradation. The present work has found that the predictions made from our models match ground truth with high accuracy. In addition, accuracy does not degrade when fewer data channels or lower resolutions are used. |
