DocumentCode :
3132079
Title :
Probabilistic structure from sound and probabilistic sound source localization
Author :
Lin, Chi-Hao ; Wang, Chieh-Chih
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei
fYear :
2008
fDate :
23-25 Aug. 2008
Firstpage :
1
Lastpage :
6
Abstract :
Auditory perception is one of the most important functions for robotics applications. Microphone arrays are widely used for auditory perception in which the spatial structure of microphones is usually known. The structure from sound (SFS) approach addresses the problem of simultaneously localizing a set of microphones and a set of acoustic events which provides a great flexibility to calibrate different setups of microphone arrays. However, the existing method does not take measurement uncertainty into account and does not provide uncertainty estimates of the SFS results. In this paper, we propose a probabilistic structure from sound (PSFS) approach using the unscented transform. In addition, a probabilistic sound source localization (PSSL) approach using the PSFS results is provided to improve sound source localization accuracy. The ample results of simulation and experiments using low cost, off-the-shell microphones demonstrate the feasibility and performance of the proposed PSFS and PSSL approaches.
Keywords :
acoustic generators; hearing; probability; robots; auditory perception; microphone arrays; off-the-shell microphones; probabilistic sound source localization; probabilistic structure from sound; robotics applications; unscented transform; Acoustic noise; Calibration; Delay estimation; Measurement uncertainty; Microphone arrays; Phased arrays; Position measurement; Robots; Sensor arrays; Working environment noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced robotics and Its Social Impacts, 2008. ARSO 2008. IEEE Workshop on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-2674-4
Electronic_ISBN :
978-1-4244-2675-1
Type :
conf
DOI :
10.1109/ARSO.2008.4653584
Filename :
4653584
Link To Document :
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