Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
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))
  • Received : 2021.10.15
  • Accepted : 2022.04.26
  • Published : 2022.06.25
⟨ Previous Next ⟩

Abstract

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.

Keywords

Acknowledgement

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.

References

  1. Bai, M.R., Ih, J.G. and Benesty, J. (2013), Acoustic Array Systems: Theory, Implementation, and Applications, John Willey & Sons.
  2. Brandstein, M. and Ward, D. (2013), Microphone Arrays: Signal Processing Techniques and Applications, Springer Science & Business Media.
  3. Castellini, P. and Martarelli, M. (2008), "Acoustic beamforming: Analysis of uncertainty and metrological performances", Mech. Syst. Signal Pr., 22, 672-692. https://doi.org/10.1016/j.ymssp.2007.09.017.
  4. Kassab, S., Michel, F. and Maxit, L. (2019), "Water experiment for assessing vibroacoustic beamforming gain for acoustic leak detection in a sodium-heated steam generator", Mech. Syst. Signal Pr., 134, 106332. https://doi.org/10.1016/j.ymssp.2019.106332.
  5. Kingma, D.P. and Ba, J. (2014), "Adam: A method for stochastic optimization", arXiv preprint arXiv:1412.6980.
  6. Kujawski, A., Herold, G. and Sarradj, E. (2019), "A deep learning method for grid-free localization and quantification of sound sources", J. Acoust. Soc. Am., 146, EL225-EL231. https://doi.org/10.1121/1.5126020.
  7. Lee, S.Y., Chang, J. and Lee, S. (2021a), "Deep learning-enhanced single point sound source localization for spherical microphone array", INTER-NOISE and NOISE-CON Congress and Conference Proceedings, 263, 2279-2283.
  8. Lee, S.Y., Chang, J. and Lee, S. (2021b), "Deep learning-based method for multiple sound source localization with high resolution and accuracy", Mech. Syst. Signal Pr., 161, 107959. https://doi.org/10.1016/j.ymssp.2021.107959.
  9. Lylloff, O. and Fernandez-Grande, E. (2018), "Noise quantification with beamforming deconvolution: effects of regularization and boundary conditions", The 7th Berlin Beamforming Conference, Berlin, March.
  10. Lylloff, O., Fernandez-Grande, E., Agerkvist, F., Hald, J., Roig, E.T. and Andersen, M.S. (2015), "Improving the efficiency of deconvolution algorithms for sound source localization", J. Acoust. Soc. Am., 138, 172-180. https://doi.org/10.1121/1.4922516.
  11. Ma, W. and Liu, X. (2019), "Phased microphone array for sound source localization with deep learning", Aerosp. Syst., 2, 71-81. https://doi.org/10.1007/s42401-019-00026-w.
  12. Pillai, S.U. and Burrus, C.S. (1989), Array Signal Processing, Springer.
  13. Qi, C.R., Su, H., Mo, K. and Guibas, L.J. (2017), "PointNet: Deep learning on point sets for 3D classification and segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, July.
  14. Xu, P., Arcondoulis, E.J.G. and Lie, Y. (2020), "Deep neural network models for acoustic source localization",. The 8th Berlin Beamforming Conference, Berlin, March.