NDLI: Multiple sound sources localization using the spatially mapped GCC functions

Content Provider	IEEE Xplore Digital Library
Author	Byoungho Kwon Youngjin Park Youn-sik Park
Copyright Year	2009
Description	Author affiliation: Department of Mechanical Engineering, KAIST, Science Town, Daejeon, 305-701, Korea (Byoungho Kwon; Youngjin Park; Youn-sik Park)
Abstract	A variety of methods for sound source localization have been developed recently and applied to several applications such as surveillance, auditory scene analysis for hearing aids, robot auditory system and so on. In the previous work, we proposed the sound source localization using the spatially mapped GCC functions based on TDOA under the single source assumption for robot auditory system. However, multi-talker case is general in human-robot interaction. Therefore, multiple source localization approaches are necessary. In this paper, we confirm the feasibility of multiple sources localization of the proposed method with white noise signals. Experimental results with white noise signals show that it is possible for the proposed method to estimate the multiple sources location when sources are uncorrelated and we can kwon that peak value of the summed GCC function depends on the source power. Moreover, experiments with human speeches in the real environment are carried out to evaluate the performance of the proposed method. These results observe that the proposed method can estimate the multiple source locations precisely and simultaneously.
Starting Page	1773
Ending Page	1776
File Size	706442
Page Count	4
File Format	PDF
ISBN	9784907764340
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-18
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	SICE
Subject Keyword	Robots Auditory system Microphones Mechanical engineering Cities and towns Humans Position measurement Ear Surveillance Image analysis robot auditory system multiple sources localization time delay of arrival (TDOA) generalized cross correlation (GCC)
Content Type	Text
Resource Type	Article

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