2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-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
SessionAUD-17: Modeling, Analysis and Synthesis of Acoustic Environments 3: Acoustic Analysis
LocationGather.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
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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.