Aiming at the shortcomings of Gaussian mixture model background method, a moving object detection method mixed with adaptive iterative block and interval frame difference method in the Gaussian mixture model is proposed. In this method, the video sequences are divided into different size pieces in order to reduce the amount of calculation of the algorithm. It not only effectively solves the pro...

In this paper, we study denoising of multicomponent images. The presented procedures are spatial wavelet-based denoising techniques, based on Bayesian leastsquares optimization procedures, using prior models for the wavelet coefficients that account for the correlations between the spectral bands. We analyze three mixture priors: Gaussian scale mixture models, Bernoulli-Gaussian mixture models ...

This work addresses the multi-target tracking problem in the nonlinear Gaussian system. One probability hypothesis density filtering algorithm based on GaussianHermite numerical integration is proposed. In order to calculate integrations in the Gaussian mixture probability hypothesis density filter, the Gaussian-Hermite numerical integration method is used to approximate the integration. In the...

Nonnative speech recognition is becoming more and more important as many speech applications are deployed world wide. Meanwhile, due to the large population of nonnative speakers, speaker adaptation remains the most practical way for providing high performance speech services. Subspace Gaussian Mixture Model (SGMM) has recently been shown to yield superior performance on various native speech r...

In developing speech recognition based services for any task domain, it is necessary to account for the support of an increasing number of languages over the life of the service. This paper considers a small vocabulary speech recognition task in multiple Indian languages. To configure a multi-lingual system in this task domain, an experimental study is presented using data from two linguistical...

ÐAn unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of independent, non-Gaussian densities. The algorithm estimates the density of each class and is able to model class distributions with non-Gaussian structure. The new algorithm can improve classification accuracy compar...

Under the Bayesian Ying-Yang (BYY) harmony learning theory, a harmony function has been developed for Gaussian mixture model with an important feature that, via its maximization through a gradient learning rule, model selection can be made automatically during parameter learning on a set of sample data from a Gaussian mixture. This paper proposes two further gradient learning rules, called conj...

We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appea...

Mixture of Gaussian processes models extended a single Gaussian process with ability of modeling multi-modal data and reduction of training complexity. Previous inference algorithms for these models are mostly based on Gibbs sampling, which can be very slow, particularly for large-scale data sets. We present a new generative mixture of experts model. Each expert is still a Gaussian process but ...

Knowledge of the noise probability density function (PDF) is central in signal detection problems, not only for optimum receiver structures but also for processing procedures such as power normalization. Unfortunately, the statistical knowledge must be acquired since the classical assumption of a Gaussian noise PDF is often not valid in underwater acoustics. In this report, we study statistical...