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Azimuthal source localization using interaural coherence in a robotic dog: modeling and application

Published online by Cambridge University Press:  15 January 2010

Rong Liu  and
Yongxuan Wang
Rong Liu*
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, P.R. China School of Electronic and Information Engineering, Dalian University of Technology, Dalian 116024, P.R. China
Yongxuan Wang
Affiliation:
School of Electronic and Information Engineering, Dalian University of Technology, Dalian 116024, P.R. China
*
*Corresponding author. E-mail: rliu@dlut.edu.cn
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Summary

In nature, sounds from multiple sources, as well as reflections from the surfaces of the physical surroundings, arrive concurrently from different directions at the ears of a listener. Despite the fact that all of these waveforms sum at the eardrums, humans with normal hearing can effortlessly segregate interesting sounds from echoes and other sources of background noises. This paper presents a two-microphone technique for localization of sound sources to effectively guide robotic navigation. Its fundamental structure is adopted from a binaural signal-processing scheme employed in biological systems for the localization of sources using interaural time differences (ITDs). The two input signals are analyzed for coincidences along left/right-channel delay-line pairs. The coincidence time instants are presented as a function of the interaural coherence (IC). Specifically, we build a sphere head model for the selected robot and apply the mechanism of binaural cues selection observed in mammalian hearing system to mitigate the effects of sound echoes. The sound source is found by determining the azimuth at which the maximum of probability density function (PDF) of ITD cues occurs. This eliminates the localization artifacts found during tests. The experimental results of a systematic evaluation demonstrate the superior performance of the proposed method.

Keywords

Sound localizationInteraural coherenceCross-correlationHead model
Type
Article
Information
Robotica , Volume 28 , Issue 7 , December 2010 , pp. 1013 - 1020
Copyright
Copyright © Cambridge University Press 2010

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