DOI QR코드

DOI QR Code

Point-level deep learning approach for 3D acoustic source localization

  • Lee, Soo Young (Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH)) ;
  • Chang, Jiho (Korea Research Institute of Standards and Science (KRISS)) ;
  • Lee, Seungchul (Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH))
  • 투고 : 2021.10.15
  • 심사 : 2022.04.26
  • 발행 : 2022.06.25

초록

Even though several deep learning-based methods have been applied in the field of acoustic source localization, the previous works have only been conducted using the two-dimensional representation of the beamforming maps, particularly with the planar array system. While the acoustic sources are more required to be localized in a spherical microphone array system considering that we live and hear in the 3D world, the conventional 2D equirectangular map of the spherical beamforming map is highly vulnerable to the distortion that occurs when the 3D map is projected to the 2D space. In this study, a 3D deep learning approach is proposed to fulfill accurate source localization via distortion-free 3D representation. A target function is first proposed to obtain 3D source distribution maps that can represent multiple sources' positional and strength information. While the proposed target map expands the source localization task into a point-wise prediction task, a PointNet-based deep neural network is developed to precisely estimate the multiple sources' positions and strength information. While the proposed model's localization performance is evaluated, it is shown that the proposed method can achieve improved localization results from both quantitative and qualitative perspectives.

키워드

과제정보

The research was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea Government Ministry of Science and ICT (MSIT) (No. 2020R1A2C1009744), in part by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2019-0-01906, Artificial Intelligence Graduate School Program (POSTECH)), in part by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) Grant funded by the Korean Government [Ministry of Trade, Industry, and Energy (MOTIE)] under Grant 20206610100290, and in part by the Fundamental Research Program of the Korea Research Institute of Standards and Science.

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