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Prior area searching for energy-based sound source localization

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Abstract

Traditional energy-based sound source localization methods have the problems of the large solution space and time-consuming calculation. Accordingly, this paper proposes to use the data collected by each acoustic sensor and their corresponding weights to adaptively initialize the prior area of a target. In this way, the potential existence range of the target is reduced and the location estimate can be determined in a small area. Specifically, we first determine the initial search point based on the current sound data and the set rules. Then, the prior location of the target is iteratively searched according to different sound energy circles’ weights. Next, the prior area of the target is determined around the prior location. Finally, the precise location of the target is further traversed to minimize the objective function, which is constructed by the weighted nonlinear least squares location (WNLS) algorithm. A series of indoor experiments are performed. The results show that our method can effectively improve the positioning accuracy by approximately 13% and greatly reduce the calculation time.

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References

  1. Yin S, Zhou B, Yang M J, et al. Localizing object parts in 3D from a single image. Sci China Inf Sci, 2019, 62: 074101

    Article  MathSciNet  Google Scholar 

  2. Usman M, Keyrouz F, Diepold K. Real time humanoid sound source localization and tracking in a highly reverberant environment. In: Proceedings of the 9th International Conference on Signal Processing, Beijing, 2008. 2661–2664

  3. Wang Q H, Ivanov T, Aarabi P. Acoustic robot navigation using distributed microphone arrays. Inf Fusion, 2004, 5: 131–140

    Article  Google Scholar 

  4. Faraji M M, Shouraki S B, Iranmehr E, et al. Sound source localization in wide-range outdoor environment using distributed sensor network. IEEE Sens J, 2020, 20: 2234–2246

    Article  Google Scholar 

  5. Yue X H, Deng F, Xu Y. Multidimensional zero-crossing interval points: a low sampling rate acoustic fingerprint recognition method. Sci China Inf Sci, 2019, 62: 019202

    Article  Google Scholar 

  6. Wang F L, Wu D, Jin P, et al. A flexible skin-mounted wireless acoustic device for bowel sounds monitoring and evaluation. Sci China Inf Sci, 2019, 62: 202402

    Article  Google Scholar 

  7. Makantasis K, Liapis A, Yannakakis G N. The pixels and sounds of emotion: general-purpose representations of arousal in games. IEEE Trans Affective Comput, 2021. doi: https://doi.org/10.1109/TAFFC.2021.3060877

  8. Hao H-K, Liang H-M, Liu Y-W. Particle methods for realtime sound source localization based on the multiple signal classification algorithm. In: Proceedings of International Conference on Intelligent Green Building and Smart Grid, Taipei, 2014. 1–5

  9. Gao S, Liu R, Wu X, et al. Eigen beam based sound source localization algorithms evaluation on a non-spherical microphone array. In: Proceedings of IEEE 2nd International Conference on Information Communication and Signal Processing, Weihai, 2019. 185–189

  10. Do H T. Robust cross-correlation-based methods for sound-source localization and separation using a large-aperture microphone array. Dissertation for Ph.D. Degree. Providence: Brown University, 2011

    Google Scholar 

  11. Deng F, Guan S, Yue X, et al. Energy-based sound source localization with low power consumption in wireless sensor networks. IEEE Trans Ind Electron, 2017, 64: 4894–4902

    Article  Google Scholar 

  12. Asono F, Asoh H, Matsui T. Sound source localization and signal separation for office robot JiJo-2. In: Proceedings of International Conference on Multisensor Fusion and Integration for Intelligent Systems, Taipei, 1999. 243–248

  13. Nakamura K, Nakadai K, Asano F, et al. Intelligent sound source localization and its application to multimodal human tracking. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, 2011. 143–148

  14. da Silva B, Segers L, Braeken A, et al. Runtime reconfigurable beamforming architecture for real-time sound-source localization. In: Proceedings of the 26th International Conference on Field Programmable Logic and Applications, Lausanne, 2016. 1–4

  15. Salvati D, Drioli C, Ferrin G, et al. Beamforming-based acoustic source localization and enhancement for multirotor UAVs. In: Proceedings of the 26th European Signal Processing Conference, Rome, 2018. 987–991

  16. Sun S, Ye Z. Robust adaptive beamforming based on a method for steering vector estimation and interference covariance matrix reconstruction. Signal Processing, 2021, 182: 107939

    Article  Google Scholar 

  17. Li J Z, Guo F C, Jiang W L. Source localization and calibration using TDOA and FDOA measurements in the presence of sensor location uncertainty. Sci China Inf Sci, 2014, 57: 042315

    Article  Google Scholar 

  18. Ho K C, Lu X, Kovavisaruch L. Source localization using TDOA and FDOA measurements in the presence of receiver location errors: analysis and solution. IEEE Trans Signal Process, 2007, 55: 684–696

    Article  MathSciNet  Google Scholar 

  19. Kim C-T, Choi T-Y, Choi B, et al. Robust estimation of sound direction for robot interface. In: Proceedings of IEEE International Conference on Robotics and Automation, Pasadena, 2008. 3475–3480

  20. Badali A, Valin J-M, Michaud F, et al. Evaluating real-time audio localization algorithms for artificial audition in robotics. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, 2009. 2033–2038

  21. Horiike D, Scheibler R, Kinoshita Y, et al. Energy-based multiple source localization with blinkies. In: Proceedings of Asia Pacific Signal and Information Processing Association Annual Summit and Conference, Auckland, 2020. 443–448

  22. Tang R, Zuo Y, Liu W, et al. Efficient energy-based orthogonal matching pursuit algorithm for multiple sound source localization with unknown source count. Meas Sci Technol, 2022, 33: 045018

    Article  Google Scholar 

  23. Lima M V S, Martins W A, Nunes L O, et al. A volumetric SRP with refinement step for sound source localization. IEEE Signal Process Lett, 2014, 22: 1098–1102

    Article  Google Scholar 

  24. Sheng X H, Hu Y-H. Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Trans Signal Process, 2004, 53: 44–53

    Article  MathSciNet  Google Scholar 

  25. Hafezi S, Moore A H, Naylor P A. 3D acoustic source localization in the spherical harmonic domain based on optimized grid search. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, 2016. 415–419

  26. You Y, Yoo J, Cha H. Event region for effective distributed acoustic source localization in wireless sensor networks. In: Proceedings of IEEE Wireless Communications and Networking Conference, Hong Kong, 2007. 2762–2767

  27. Jiang X, Li S, Luo B, et al. Source exploration for an under-actuated system: a control-theoretic paradigm. IEEE Trans Contr Syst Technol, 2019, 28: 1100–1107

    Article  Google Scholar 

  28. Li S, Guo Y. Distributed source seeking by cooperative robots: all-to-all and limited communications. In: Proceedings of IEEE International Conference on Robotics and Automation, Saint Paul, 2012. 1107–1112

  29. Li S, Wang Z, Li Y. Using Laplacian eigenmap as heuristic information to solve nonlinear constraints defined on a graph and its application in distributed range-free localization of wireless sensor networks. Neural Process Lett, 2013, 37: 411–424

    Article  Google Scholar 

  30. Saric Z M, Kukolj D D, Teslic N D. Acoustic source localization in wireless sensor network. Circ Syst Signal Process, 2010, 29: 837–856

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by National Science Fund for Distinguished Young Scholars of China (Grant No. 62025301), Basic Science Center Project (Grant No. 62088101), and Key Program of National Natural Science Foundation of China (Grant No. 61933002).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081, China

    Feng Gao, Yeyun Cai, Fang Deng, Chengpu Yu & Jie Chen

  2. Beijing Institute of Technology Chongqing Innovation Center, Chongqing, 401120, China

    Feng Gao, Yeyun Cai, Fang Deng & Chengpu Yu

  3. Guohao Academy, Tongji University, Shanghai, 200092, China

    Jie Chen

Authors
  1. Feng Gao
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  2. Yeyun Cai
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  3. Fang Deng
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  4. Chengpu Yu
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  5. Jie Chen
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Correspondence to Fang Deng.

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Gao, F., Cai, Y., Deng, F. et al. Prior area searching for energy-based sound source localization. Sci. China Inf. Sci. 65, 222204 (2022). https://doi.org/10.1007/s11432-022-3568-2

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  • DOI: https://doi.org/10.1007/s11432-022-3568-2

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