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ICASSP
2009
IEEE
13 years 7 months ago
Blind sparse source separation for unknown number of sources using Gaussian mixture model fitting with Dirichlet prior
In this paper, we propose a novel sparse source separation method that can be applied even if the number of sources is unknown. Recently, many sparse source separation approaches ...
Shoko Araki, Tomohiro Nakatani, Hiroshi Sawada, Sh...
GLOBECOM
2006
IEEE
14 years 4 months ago
Feedback Capacity of Stationary Sources over Gaussian Intersymbol Interference Channels
Abstract— We consider discrete-time channels with finitelength intersymbol interference and additive Gaussian noise. The channel noise is considered to be a stationary ARMA (aut...
Shaohua Yang, Aleksandar Kavcic, Sekhar Tatikonda
ICDE
2009
IEEE
165views Database» more  ICDE 2009»
14 years 11 months ago
Spatial Range Querying for Gaussian-Based Imprecise Query Objects
Abstract-- In sensor environments and moving robot applications, the position of an object is often known imprecisely because of measurement error and/or movement of the object. In...
Yoshiharu Ishikawa, Yuichi Iijima, Jeffrey Xu Yu
ICIP
2001
IEEE
14 years 11 months ago
EM algorithms of Gaussian mixture model and hidden Markov model
The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Mo...
Guorong Xuan, Wei Zhang, Peiqi Chai
ICASSP
2009
IEEE
14 years 4 months ago
Bounded conditional mean imputation with Gaussian mixture models: A reconstruction approach to partly occluded features
In this work we show how conditional mean imputation can be bounded through the use of box-truncated Gaussian distributions. That is of interest when signals or features are partl...
Friedrich Faubel, John W. McDonough, Dietrich Klak...