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 IDCI-2.6
Paper Title SYNTHETIC APERTURE ACOUSTIC IMAGING WITH DEEP GENERATIVE MODEL BASED SOURCE DISTRIBUTION PRIOR
Authors Boqiang Fan, Rice University, United States; Samarjit Das, Bosch Research Pittsburgh, United States
SessionCI-2: Computational Imaging for Inverse Problems
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Computational Imaging: [CIS] Computational Imaging Systems
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Acoustic imaging has a wide range of real-world applications such as machine health monitoring. Conventionally, large microphone arrays are utilized to achieve useful spatial resolution in the imaging process. The advent of location-aware autonomous mobile robotic platforms opens up unique opportunity to apply synthetic aperture techniques to the acoustic imaging problem. By leveraging motion and location cues as well as some available prior information on the source distribution, a small moving microphone array has the potential to achieve imaging resolution far beyond the physical aperture limits. In this work, we propose to image large acoustic sources with a combination of synthetic aperture and their geometric structures modeled by a conditional generative adversarial network (cGAN). The acoustic imaging problem is formulated as a linear inverse problem and solved with the gradient-based method. Numerical simulations show that our synthetic aperture imaging framework can reconstruct the acoustic source distribution from microphone recordings and outperform static microphone arrays.