We introduce a mixture of generalized hyperbolic distributions as an alternative to the ubiquitous mixture of Gaussian distributions as well as their near relatives of which the mixture of multivariate t and skew-t distributions are predominant. The mathematical development of our mixture of generalized hyperbolic distributions model relies on its relationship with the generalized inverse Gauss...

Music tags describe different types of semantic information of music. In this paper, we present our submission to the audio tag classification task in MIREX 2010. We propose a posterior weighted Bernoulli mixture model (PWBMM) to automatically annotate a song with tags. The PWBMM approach uses a Gaussian mixture modelbased posterior representation to characterize the 70dimensional music feature...

The efficiency of many speech processing methods rely on accurate modeling of the distribution of the signal spectrum and a majority of prior works suggest that the spectral components follow the Laplace distribution. To improve the probability distribution models based on our knowledge of speech source modeling, we argue that the model should in fact be a multiplicative mixture model, includin...

The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular in cluster analysis. Their estimation is in general performed by maximum likelihood estimation and has also been considered from a parametric Bayesian prospective. We propose new Dirichlet Process Parsimonious mixtur...

Codebook design for vector quantization could be performed using clustering technique. The Gaussian Mixture Modeling (GMM) clustering algorithm involves modeling a statistical distribution by a mixture (or weighted sum) of other distributions. GMM has proven superior efficiency in both time and accuracy and has been used with vector quantization in some applications. This paper introduces a med...

Title of dissertation: GLOBAL OPTIMIZATION OF FINITE MIXTURE MODELS Jeffrey W. Heath Doctor of Philosophy, 2007 Dissertation directed by: Professor Michael Fu Robert H. Smith School of Business & Professor Wolfgang Jank Robert H. Smith School of Business The Expectation-Maximization (EM) algorithm is a popular and convenient tool for the estimation of Gaussian mixture models and its natural ext...

In this paper, we propose a novel boosted mixture learning (BML) framework for Gaussian mixture HMMs in speech recognition. BML is an incremental method to learn mixture models for classification problem. In each step of BML, one new mixture component is calculated according to functional gradient of an objective function to ensure that it is added along the direction to maximize the objective ...

A convenient way of modelling complex interactions is by employing graphs or networks which correspond to conditional independence structures in an underlying statistical model. One main class of models in this regard are Bayesian networks, which have the drawback of making parametric assumptions. Bayesian nonparametric mixture models offer a possibility to overcome this limitation, but have ha...