1.1 Classification Model Before presenting in more details the Gaussian Mixture Model (GMM) classification process, it is worthwhile to consider what “classification” actually means. According to [3], a “classification model” is made of three main parts : • a transducer : in the case of music this would typically be the A/D conversion chain of the sound. • a feature extractor : it extracts sign...

This paper presents convergence results for the Box Gaussian Mixture Filter (BGMF). BGMF is a Gaussian Mixture Filter (GMF) that is based on a bank of Extended Kalman Filters. The critical part of GMF is the approximation of probability density function (pdf) as pdf of Gaussian mixture such that its components have small enough covariance matrices. Because GMF approximates prior and posterior a...

Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually complex and further needs to be simplified. In this paper, we present a GMM simplification method based on a hierarchical clustering algorith...

We compare two regularization methods which can be used to improve the generalization capabilities of Gaussian mixture density estimates. The rst method consists of deening a Bayesian prior distribution on the parameter space. We derive EM (Expectation Maximization) update rules which maximize the a posterior parameter probability in contrast to the usual EM rules for Gaussian mixtures which ma...

Many information fusion tasks involve the processing of Gaussian mixtures with simple underlying shape, but many components. This paper addresses the problem of reducing the number of components, allowing for faster density processing. The proposed approach is based on identifying components irrelevant for the overall density’s shape by means of the curvature of the density’s surface. The key i...

This paper investigates the problem of verifying the pronunciations of phonemes from continuous utterances collected from impaired children speakers engaged in a speech therapy session. A new pronunciation verification (PV) approach based on the subspace Gaussian mixture model (SGMM) is presented. A single SGMM is trained from test utterances collected from impaired and unimpaired speakers. PV ...

In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking. The proposed method, called the Gaussian mixture measurements-probability hypothesis density (GMM-PHD) filter, not only approximates the posterior intensity using a Gaussian mixture, but also models the likelihood functi...