Variance estimation and ranking methods are developed for stochastic processes modeled by Gaussian mixture distributions. It is shown that the variance estimate from a Gaussian mixture distribution has the same properties as a variance estimate from a single Gaussian distribution based on a reduced number of samples. Hence, well known tools of variance estimation and ranking of single Gaussian ...

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...

We propose an extension of the mixture of factor (or independent component) analyzers model to include strongly super-gaussian mixture source densities. This allows greater economy in representation of densities with (multiple) peaked modes or heavy tails than using several Gaussians to represent these features. We derive an EM algorithm to find the maximum likelihood estimate of the model, and...

a parallel hybrid system of hmm and gmm modeling techniques was implemented and used in a telephony speaker verification and identification system. spectral subtraction and weighted projection measure were used to render this system more robust against additional noise. cepstral mean subtraction method was also applied for the compensation of convolution noise due to transmission channel degrad...

Consider the estimation of a signal x ∈ R from noisy observations r = x+ z, where the input x is generated by an independent and identically distributed (i.i.d.) Gaussian mixture source, and z is additive white Gaussian noise (AWGN) in parallel Gaussian channels. Typically, the `2-norm error (squared error) is used to quantify the performance of the estimation process. In contrast, we consider ...

The mixture of Gaussian distributions, a soft version of k-means ( [2]), is considered a stateof-the-art clustering algorithm. It is widely used in computer vision for selecting classes, e.g., color[4, 1, 5],texture[1, 9], shapes [12, 10]. In this algorithm, each class is described by a Gaussian distribution, defined by its mean and covariance. The data is described by a weighted sum of these G...

This paper proposes the classification of emotions based on spectral features using the Gaussian Mixture Model as the classifier. The performance of the Gaussian Mixture Model has been evaluated for two types of databases – acted and reallife speech corpuses. The model has also been evaluated for the variation in its performance based on the speaker, gender of the speaker and the number of the ...

The problem of merging Gaussian mixture components is discussed in situations where a Gaussian mixture is fitted but the mixture components are not separated enough from each other to interpret them as “clusters”. The problem of merging Gaussian mixtures is not statistically identifiable, therefore merging algorithms have to be based on subjective cluster concepts. Cluster concepts based on uni...

Many inference problems in environmental DDDAS must contend with high dimensional models and non-Gaussian uncertainties, including but not limited to Data Assimilation, Targeting and Planning. In this this paper, we present the Mixture Ensemble Filter (MEnF) which extends ensemble filtering to non-Gaussian inference using Gaussian mixtures. In contrast to the state of the art, MEnF embodies an ...