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» Simplifying Mixture Models Using the Unscented Transform
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ICASSP
2011
IEEE
12 years 11 months ago
Dirichlet Mixture Models of neural net posteriors for HMM-based speech recognition
In this paper, we present a novel technique for modeling the posterior probability estimates obtained from a neural network directly in the HMM framework using the Dirichlet Mixtu...
Balakrishnan Varadarajan, Garimella S. V. S. Sivar...
ISBI
2007
IEEE
14 years 2 months ago
A Continuous Mixture of Tensors Model for Diffusion-Weighted Mr Signal Reconstruction
Diffusion MRI is a non-invasive imaging technique that allows the measurement of water molecular diffusion through tissue in vivo. In this paper, we present a novel statistical mo...
Bing Jian, Baba C. Vemuri, Evren Özarslan, Pa...
WSC
2007
13 years 10 months ago
Classification analysis for simulation of machine breakdowns
Machine failure is often an important factor in throughput of manufacturing systems. To simplify the inputs to the simulation model for complex machining and assembly lines, we ha...
Lanting Lu, Christine S. M. Currie, Russell C. H. ...
ICASSP
2008
IEEE
14 years 2 months ago
Singer melody extraction in polyphonic signals using source separation methods
We propose a new approach for singer melody extraction, based on blind source separation techniques. The short time Fourier transform (STFT) of the singer signal is modelled by a ...
Jean-Louis Durrieu, Gaël Richard, Bertrand Da...
BMCBI
2007
138views more  BMCBI 2007»
13 years 8 months ago
A full Bayesian hierarchical mixture model for the variance of gene differential expression
Background: In many laboratory-based high throughput microarray experiments, there are very few replicates of gene expression levels. Thus, estimates of gene variances are inaccur...
Samuel O. M. Manda, Rebecca E. Walls, Mark S. Gilt...