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ICASSP
2009
IEEE
15 years 1 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...
117
Voted
ICASSP
2007
IEEE
15 years 9 months ago
Query by Example of Audio Signals using Euclidean Distance Between Gaussian Mixture Models
Query by example of multimedia signals aims at automatic retrieval of media samples from a database, which are similar to a userprovided example. This paper proposes a method for ...
Marko Helén, Tuomas Virtanen
WSC
1998
15 years 4 months ago
Bayesian Model Selection when the Number of Components is Unknown
In simulation modeling and analysis, there are two situations where there is uncertainty about the number of parameters needed to specify a model. The first is in input modeling w...
Russell C. H. Cheng
ATAL
2007
Springer
15 years 7 months ago
Confidence-based policy learning from demonstration using Gaussian mixture models
We contribute an approach for interactive policy learning through expert demonstration that allows an agent to actively request and effectively represent demonstration examples. I...
Sonia Chernova, Manuela M. Veloso
114
Voted
ICPR
2002
IEEE
16 years 4 months ago
Visual Abstraction of Wildlife Footage Using Gaussian Mixture Models and the Minimum Description Length Criterion
bstraction of Wildlife Footage using Gaussian Mixture Models and the Minimum Description Length Criterion David Gibson Neill Campbell Barry Thomas Department of Computer Science Un...
David P. Gibson, Neill W. Campbell, Barry T. Thoma...