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JRM Vol.29 No.1 pp. 49-58
doi: 10.20965/jrm.2017.p0049
(2017)

Paper:

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Active Sound Source Localization by Pinnae with Recursive Bayesian Estimation

Wataru Odo*1, Daisuke Kimoto*2, Makoto Kumon*3, and Tomonari Furukawa*4

*1Sumitomo Chemical, Co., Ltd.
2200 Tsurusaki, Oita 870-0106, Japan

*2Daihatsu Motor Kyushu Co., Ltd.
1 Showashinden, Nakatsu, Oita 879-0107, Japan

*3Kumamoto University
2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan

*4Virginia Tech
Blacksburg, VA 24061, USA

Received:
July 20, 2016
Accepted:
September 3, 2016
Published:
February 20, 2017
Creative Commons License
Keywords:
binaural robots, sound source localization, active pinnae, recursive Bayesian estimation
Abstract
Animals use two ears to localize the source of a sound, and this paper considers a robot system that localizes a sound source by using two microphones with active external reflectors that mimic movable pinnae. The body of the robot and the environment both affect the propagation of sound waves, which complicates mapping the acoustic cues to the source. The mapping may be multimodal, and the observed acoustic cues may lead to the incorrect estimation of the locations. In order to achieve sound source localization with such multimodal likelihoods, this paper presents a method for determining a configuration of active pinnae, which uses prior knowledge to optimize their location and orientation, and thus attenuates the effects of pseudo-peaks in the observations. The observations are also adversely affected by noise in the sensor signals, and thus Bayesian inference approach to process them is further introduced. Results of experiments that validate the proposed method are also presented.
Schematic of the proposed system for actively localizing the sound source

Schematic of the proposed system for actively localizing the sound source

Cite this article as:
W. Odo, D. Kimoto, M. Kumon, and T. Furukawa, “Active Sound Source Localization by Pinnae with Recursive Bayesian Estimation,” J. Robot. Mechatron., Vol.29 No.1, pp. 49-58, 2017.
Data files:
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