GMM/UBM framework is wildly used in Automatic Speaker Verification (ASV), however, due to the insufficiency of the training data, both the hypothesized speaker and impostors are not well modeled, especially to some of the Gaussian component mixtures. Thus, the Gaussian mixtures in each GMM model have different discriminative capabilities, and the mismatch between testing and training data will ...

The goal of image segmentation is to cluster the pixels of an image into several regions. This article describes the method of image segmentation using Artificial Bee Colony Optimization (ABC). This optimization technique is motivated by intelligent behaviour of honey bees and it provides a population based search procedure. In this article Gaussian Mixture Model (GMM) is used and each pixel cl...

Speaker identification is an important activity in the process of speaker diarization. We need to model the speaker by Gaussian mixture model (GMM) for speaker identification purpose. Large GMM is called as a Universal Background Model (UBM) which is adapted into each speaker model for speaker identification purpose. This paper focuses on speech clustering for speaker diarization. The speaker d...

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...

—Gaussian Mixture Models (GMMs) are the most popular techniques in background modeling but present some limitations when some dynamic changes occur like camera jitter, illumination changes, movement in the background. Furthermore, the GMM are initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate fal...

Today, digital audio applications are part of our everyday lives. Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. The accuracy of the classification relies on the strength of the features and classification scheme. In this work both, time domain and frequency domain features are extracted from the input signal. Time d...

This work provides two statistical Gaussian forecasting methods for predicting First Daily Departure Times (FDDTs) of everyday use electric vehicles. This is important in smart grid applications to understand disconnection times of such mobile storage units, for instance to forecast storage of non dispatchable loads (e.g. wind and solar power). We provide a review of the relevant state-of-the-a...

Urban traffic forecasting models generally follow either a Gaussian Mixture Model (GMM) or Support Vector Classifier (SVC) to estimate the features of potential road accidents. Although SVC can provide good performances with less data than GMM, it incurs higher computational cost. This paper proposes novel framework that combines descriptive strength high-performance classification capabilities...

We propose a Gaussian mixture model (GMM)-based approach to discriminate stationary humans from their ghosts and clutter in through-the-wall radar images. More specifically, we use a mixture of Gaussian distributions to model the image intensity histograms corresponding to target and ghost/clutter regions. The mixture parameters, namely the means, variances, and weights of the component distrib...

This paper presents a new approach to exploit data-driven universal background model (UBM) generation using tied Gaussians for accent identification (AID). The motivation of the proposed algorithm is to potentially utilize broad phoneticspecific accent characteristics by Gaussian mixture model (GMM) and examine data-driven phonetically-inspired UBM creation for GMM-supervector based accent clas...