We present an algorithm for tracking many objects observed with distributed, non-overlapping sensors. Our method is derived from a proposition that the observations of some constant, intrinsic properties of an object form a cluster (eg. in the color space). However sensors also provide dynamic data about an object like time and location. Tracking is achieved by probabilistic clustering of obser...

In this paper,features for text-independent speaker recognition has been evaluated. Speaker identification from a set of templates and analyzing speaker recognition rate by extracting several key features like Mel Frequency Cepstral Coefficients [MFCC] from the speech signals of those persons by using the process of feature extraction using MATLAB2013 .These features are effectively captured us...

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...

Gaussian Mixture Model (GMM) is one of the most popular data clustering methods which can be viewed as a linear combination of different Gaussian components. In GMM, each cluster obeys Gaussian distribution and the task of clustering is to group observations into different components through estimating each cluster’s own parameters. The ExpectationMaximization algorithm is always involved in su...

By a “covering” we mean a Gaussian mixture model fit to observed data. Approximations of the Bayes factor can be availed of to judge model fit to the data within a given Gaussian mixture model. Between families of Gaussian mixture models, we propose the Rényi quadratic entropy as an excellent and tractable model comparison framework. We exemplify this using the segmentation of an MRI image volu...

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 ...

In the field of human computer interaction automatic speech emotion recognition is a current research topic. Emotion recognition in speech is a challenging problem because it is unclear that which features are effective for speech emotion recognition. In this paper we proposed an approach in which we extract the features of energy, spectral and acoustic domains and then merging these features b...

This project centers on the investigation of appl-ying Gaussian Mixture Model (GMM) to supervised learning based on the Maximum Lik-elihood (ML) estimation using Expectation Maximization (EM). As learnt, the statistical modeling methods manipulate probabilities dire-ctly, thus giving more sophisticated description over the actual world with its disadvantage of the expensive computational comple...

Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variabilit...

In this paper, we explore how different acoustic modeling techniques can benefit from data in languages other than the target language. We propose an algorithm to perform decision tree state clustering for the recently proposed Kullback-Leibler divergence based hidden Markov models (KL-HMM) and compare it to subspace Gaussian mixture modeling (SGMM). KLHMM can exploit multilingual information i...