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JRM Vol.29 No.1 pp. 177-187
doi: 10.20965/jrm.2017.p0177
(2017)

Paper:

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Outdoor Sound Source Detection Using a Quadcopter with Microphone Array

Takuma Ohata*, Keisuke Nakamura**, Akihide Nagamine*, Takeshi Mizumoto**, Takayuki Ishizaki*, Ryosuke Kojima*, Osamu Sugiyama*, and Kazuhiro Nakadai*,**

*Graduate School of Information Science and Engineering, Tokyo Institute of Technology
2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan

**Honda Research Institute Japan Co., Ltd.
8-1 Honcho, Wako-shi, Saitama 351-0114, Japan

Received:
July 29, 2016
Accepted:
November 30, 2016
Published:
February 20, 2017
Creative Commons License
Keywords:
robot audition, sound source detection, sound source localization, microphone array processing, unmanned aerial vehicle
Abstract
This paper addresses sound source detection in an outdoor environment using a quadcopter with a microphone array. As the previously reported method has a high computational cost, we proposed a sound source detection algorithm called multiple signal classification based on incremental generalized singular value decomposition (iGSVD-MUSIC) that detects the sound source location and temporal activity at low computational cost. In addition, to relax the estimation error problem of a noise correlation matrix that is used in iGSVD-MUSIC, we proposed correlation matrix scaling (CMS) to achieve soft whitening of noise. As CMS requires a parameter to decide the degree of whitening, we analyzed the optimal value of the parameter by using numerical simulation. The prototype system based on the proposed methods was evaluated with two types of microphone arrays in an outdoor environment. The experimental results showed that the proposed iGSVD-MUSIC-CMS significantly improves sound source detection performance, and the prototype system achieves real-time processing. Moreover, we successfully clarified the behavior of the CMS parameter by using a numerical simulation in which the empirically-obtained optimal value corresponded with the analytical result.*
* This work is an extension of our publication “Takuma Ohata et al.: Improvement in outdoor sound source detection using a quadrotor-embedded microphone array, IROS 2014, pp.1902-1907, 2014.”
System architecture for sound source detection using a quadcopter with a microphone array

System architecture for sound source detection using a quadcopter with a microphone array

Cite this article as:
T. Ohata, K. Nakamura, A. Nagamine, T. Mizumoto, T. Ishizaki, R. Kojima, O. Sugiyama, and K. Nakadai, “Outdoor Sound Source Detection Using a Quadcopter with Microphone Array,” J. Robot. Mechatron., Vol.29 No.1, pp. 177-187, 2017.
Data files:
References
  1. [1] K. Nakadai, H. G. Okuno, H. Nakajima, Y. Hasegawa, and H. Tsujino, “Design and implementation of robot audition system HARK,” Advanced Robotics, Vol.24, No.9, pp. 739-761, 2009.
  2. [2] Y. Sasaki, N. Hatao, K. Yoshii, and S. Kagami, “Nested iGMM recognition and multiple hypothesis tracking of moving sound sources for mobile robot audition,” Proc. of the IEEE/RSJ Int. Conf. on Robots and Intelligent Systems (IROS), pp. 3930-3936, 2013.
  3. [3] Y. Bando, T. Mizumoto, K. Itoyama, K. Nakadai, and H. G. Okuno, “Posture estimation of hose-shaped robot using microphone array localization,” Proc. of the IEEE/RSJ Int. Conf. on Robots and Intelligent Systems (IROS), pp. 3446-3451, 2013.
  4. [4] B. Kaushik, D. Nance, and K. K. Ahuj, “A review of the role of acoustic sensors in the modern battlefield,” Proc. of 11th AIAA/CEAS Aeroacoustics Conf. (26th AIAA Aeroacoustics Conf.), pp. 1-13, 2005.
  5. [5] H.-E. de Bree, “Acoustic vector sensors increasing UAV’s situational awareness,” SAE Technical Paper, p. 3249, 2009.
  6. [6] K. Okutani, T. Yoshida, K. Nakamura, and K. Nakadai, “Outdoor auditory scene analysis using a moving microphone array embedded in a quadrocopter,” Proc. of the IEEE/RSJ Int. Conf. on Robots and Intelligent Systems (IROS), pp. 3288-3293, 2012.
  7. [7] K. Nakamura, K. Nakadai, F. Asano, Y. Hasegawa, and H. Tsujino, “Intelligent sound source localization for dynamic environments,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 664-669, 2009.
  8. [8] R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Trans. on Antennas and Propagation, Vol.34, No.3, pp. 276-280, 1986.
  9. [9] K. Furukawa, K. Okutani, K. Nagira, T. Otsuka, K. Itoyama, K. Nakadai, and H. G. Okuno, “Noise correlation matrix estimation for improving sound source localization by multirotor UAV,” Proc. of the IEEE/RSJ Int. Conf. on Robots and Intelligent Systems (IROS), pp. 3943-3948, 2013.
  10. [10] K. Nakamura, K. Nakadai, and G. Ince, “Real-time super-resolution sound source localization for robots,” Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 694-699, 2012.
  11. [11] T. Ohata, K. Nakamura, T. Mizumoto, T. Tezuka, and K. Nakadai, “Improvement in outdoor sound source detection using a quadrotor-embedded microphone array,” Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 1902-1907, 2014.
  12. [12] K. Okutani, T. Yoshida, K. Nakamura, and K. Nakadai, “Incremental noise estimation in outdoor auditory scene analysis using a quadrocopter with a microphone array,” J. of Robotics Society of Japan, Vol.31, No.7, pp. 676-683, 2013 (in Japanese).

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