1 Introduction This report presents a novel speaker verification system that generates a new feature set that captures long duration speaker identifying characteristics while taking advantage of the well-established and well-studied Gaussian Mixture Model system (GMM). Much of the innovation in the system is contained in the intelligent exploitation of traditional cepstral features such that te...

Secondary data are important in geostatistical simulation of continuous variables. Seismic data and geological trends are used for porosity modeling. Porosity is used for permeability and residual water saturation modeling. Multiple mineral or contaminant concentrations must often be modeled for mining and environmental applications. Sequential Gaussian simulation (or some other variant of Gaus...

Voice activity detection (VAD) is a basic component of noise reduction algorithms. In this paper, we propose a voice activity detector based on a sequential Gaussian Mixture Model (SGMM) in log-spectral domain. This model comprises two Gaussian components, which respectively describe the speech and nonspeech log-power distributions. The initial distributions are firstly established by EM algori...

We revisit the sequential rate-distortion (SRD) trade-off problem for vector-valued Gauss-Markov sources with mean-squared error distortion constraints. We show via a counterexample that the dynamic reverse water-filling algorithm suggested by [1, eq. (15)] is not applicable to this problem, and consequently the closed form expression of the asymptotic SRD function derived in [1, eq. (17)] is n...

Conditional Random Fields are among the most popular techniques for image labelling because of their flexibility in modelling dependencies between the labels and the image features. This paper addresses the problem of efficient classification of partially occluded objects. For this purpose we propose a novel Gaussian Mixture Model based on a sequential training procedure, in combination with mu...

the estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. in this paper, we investigate the spatial distribution of three air pollutants in tehran’s atmosphere: carbon monoxide (co), nitrogen dioxide (no2), and atmospheric particulate matters less ...

Within the Bayesian framework, we utilize Gaussian processes for parametric studies of long running computer codes. Since the simulations are expensive, it is necessary to exploit the computational budget in the best possible manner. Employing the sum over variances —being indicators for the quality of the fit—as the utility function, we establish an optimized and automated sequential parameter...

Consider the random sequential packing model with infinite input and in any dimension. When the input consists of non-zero volume convex solids we show that the total number of solids accepted over cubes of volume λ is asymptotically normal as λ →∞. We provide a rate of approximation to the normal and show that the finite dimensional distributions of the packing measures converge to those of a ...

This paper studies the problem of sequential Gaussian binary hypothesis testing in a distributed multi-agent network. A sequential probability ratio test (SPRT) type algorithm in a distributed framework of the consensus+innovations form is proposed, in which the agents update their decision statistics by simultaneously processing latest observations (innovations) sensed sequentially over time a...