NDLI: A novel model for reverberant signals; robust maximum likelihood localization of real signals based on a sub-Gaussian model

Content Provider	IEEE Xplore Digital Library
Author	Georgiou, P.G. Kyriakakis, C.
Copyright Year	2002
Description	Author affiliation: Immersive Audio Lab., Univ. of Southern California, Los Angeles, CA, USA (Georgiou, P.G.; Kyriakakis, C.)
Abstract	We present a novel model for signals encountered in reverberant environments using the sub-Gaussian distribution that can describe both the impulsive nature of the signals and their inter-dependence. The proposed system can be viewed as one where the sources are stochastic and Gaussian and the transfer medium is varying in a highly impulsive manner, introducing the sub-Gaussian nature at the receiver or alternatively, the impulsive transformation to the signals can be viewed as part of the source model, creating a multivariate source signal whose components can not be independent, and is of impulsiveness equal to the one of the Cauchy distribution. We formulate the separable maximum likelihood solution to an array signal processing problem based on a derived sub-Gaussian density. We proceed to give both simulations and experimental results of the validity of the algorithm. In the experiments sound signals are played from loudspeakers in a room and localized with a microphone array and it is demonstrated that the localization based on the sub-Gaussian moded significantly outperforms the one based on the traditional Gaussian model.
Starting Page	1278
Ending Page	1282
File Size	358351
Page Count	5
File Format	PDF
ISBN	0780375769
ISSN	10586393
DOI	10.1109/ACSSC.2002.1196986
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Gaussian processes Gaussian distribution Maximum likelihood estimation Acoustic noise Random variables Random processes Signal processing Stochastic systems Signal processing algorithms
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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