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Active Sound Source Localization by Pinnae with Recursive Bayesian Estimation
Author(s) -
Wataru Odo,
Daisuke Kimoto,
Makoto Kumon,
Tomonari Furukawa
Publication year - 2017
Publication title -
journal of robotics and mechatronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 19
eISSN - 1883-8049
pISSN - 0915-3942
DOI - 10.20965/jrm.2017.p0049
Subject(s) - acoustic source localization , computer science , schematic , sound (geography) , robot , inference , noise (video) , bayesian probability , acoustics , artificial intelligence , bayesian inference , computer vision , process (computing) , image (mathematics) , physics , engineering , electronic engineering , operating system
[abstFig src='/00290001/05.jpg' width='300' text='Schematic of the proposed system for actively localizing the sound source' ] 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.

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