Data-driven analysis methods, such as independent component analysis (ICA) and clustering, have found a fruitful application in the analysis of functional magnetic resonance imaging (fMRI) data for identifying functionally connected brain networks. Unlike the traditional regression-based hypothesis-driven analysis methods, the principal advantage of data-driven methods is their applicability to...

This paper addresses the problem of fitting finite Gaussian Mixture Model (GMM) with unknown number of components to the univariate and multivariate data. The typical method for fitting a GMM is Expectation Maximization (EM) in which many challenges are involved i.e. how to initialize the GMM, how to restrict the covariance matrix of a component from becoming singular and setting the number of ...

Structural Health Monitoring (SHM) technology is considered to be a key technology to reduce the maintenance cost and meanwhile ensure the operational safety of aircraft structures. It has gradually developed from theoretic and fundamental research to real-world engineering applications in recent decades. The problem of reliable damage monitoring under time-varying conditions is a main issue fo...

In this paper, a class of GMM-based discriminative kernels is proposed for speaker identification. We map an utterance vector into a matrix by finding the sequence of components, which have the maximum likelihood in the GMM for the all frame vectors. And the weights matrix was used, which were got by the GMM's parameters. Then the SVMs are used for classification. A one-versus-rest fashion is u...

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...

In this paper we aim to improve the performance of Gaussian Mixture Model (GMM) classifier using Impostor model parameters for a closed set Speaker Identification task. We propose a novel method of speaker model training which uses the parameters of an Impostor Model to discriminatively train, in order to improve the performance of the GMM based classifier. This is unlike conventional technique...

Despite possible structural changes related to atrophy and edema, the structural anatomy of the brain should present time consistency for a given patient. Based on this assumption, we propose a lesion segmentation method that first derives a gaussian mixture model (GMM) separating healthy tissues from pathological and unexpected ones on a multi-time-point intra-subject groupwise image. This ave...

In this paper, a GMM with common mixture components, referred to as the common component GMM (CCGMM), is proposed to be the signal model for calculating the diversity measure for the speaker-and-environment change detection in broadcast news signal. The use of GMM is to increase the accuracy of audio signal modeling while the use of common mixture components is to solve the complexity problem o...

Voice conversion refers to the adaptation of the characteristics of a source speaker's voice to those of a target speaker. Gaussian mixture models (GMM) have been found to be efficient in the voice conversion task. The GMM parameters are estimated from a training set with the goal to minimize the mean squared error (MSE) between the transformed and target vectors. Obviously, the quality of the ...

In this project report, we have investigated the video modeling techniques and realized a statistical video representation and modeling scheme [1], which could be used for later video retrieval and content extraction task. This method utilizes Gaussian mixture model (GMM) to segment video content into coherent space-time segments within the video frames and across frames. It treats space and ti...