Speech process has benefited a great deal from the wavelet transforms. Wavelet packets decompose signals in to broader components using linear spectral bisecting. In this paper, mixtures of speech signals are decomposed using wavelet packets, the phase difference between the two mixtures are investigated in wavelet domain. In our method Laplacian Mixture Model (LMM) is defined. An Expectation M...

An extension of the Gaussian mixture model is presented using Independent Component Analysis (ICA) and the generalized Gaussian density model. The mixture model assumes that the observed data can be categorized into mutually exclusive classes whose components are generated by a linear combination of independent sources. The source densities are modeled by generalized Gaussians (Box and Tiao, 19...

In this paper, we try to find a particular combination of wavelet shrinkage and nonlinear diffusion for noise removal in dental images. We selected the wavelet diffusion and modified its automatic threshold selection by proposing new models for speckle-related modulus. The Laplacian mixture model, Rayleigh mixture model, and circular symmetric Laplacian mixture models were evaluated and, as it ...

We propose a new procedure for clustering nucleotide sequences based on the “Laplacian Eigenmaps” and Gaussian Mixture modelling. This proposal is then applied to a set of 100 DNA sequences from the mitochondrially encoded NADH dehydrogenase 3 (ND3) gene of a collection of Platyhelminthes and Nematoda species. The resulting clusters are then shown to be consistent with the gene phylogenetic tre...

Gaussian mixture model (GMM) has been widely used for data analysis in various domains including text documents, face images and genes. GMM can be viewed as a simple linear superposition of Gaussian components, each of which represents a data cluster. Recent models, namely Laplacian regularized GMM (LapGMM) and locally consistent GMM (LCGMM) have been proposed to preserve the than the original ...

In this paper, we study denoising of multicomponent images. The presented procedures are spatial wavelet-based denoising techniques, based on Bayesian leastsquares optimization procedures, using prior models for the wavelet coefficients that account for the correlations between the spectral bands. We analyze three mixture priors: Gaussian scale mixture models, Bernoulli-Gaussian mixture models ...

background: birth weight and gestational age are two important variables in obstetric research. the primary measure of gestational age is based on a mother's recall of her last menstrual period. this recall may cause random or systematic errors. therefore, the objective of this study is to utilize bayesian mixture model in order to identify implausible gestational age.   methods: in this cross-...