Although typical model-based noise suppression including the vector Taylor series-based approach employs a single Gaussian distribution for the noise model, it is insufficient for nonstationary noises which have a complex structured distribution. As a solution to this problem, we have already proposed a method for estimating a Gaussian mixture model (GMM)-based noise model by using a minimum me...

It is well-known that the performance of acoustic-to-articulatory inversion improves by smoothing the articulatory trajectories estimated using Gaussian mixture model (GMM) mapping (denoted by GMM + Smoothing). GMM + Smoothing also provides similar performance with GMM mapping using dynamic features, which integrates smoothing directly in the mapping criterion. Due to the separation between smo...

Gaussian mixture model or Gaussian mixture density model(GMM) uses the likelihood function as a measure of fit. We show that just the same algorithm as the GMM can be derived from a modified objective function of Fuzzy c-Means (FCM) clustering with the regularizer by K-L information, only when the parameter λ equals 2. Although the fixed-point iteration scheme of FCM is similar to that of the G...

The purpose of this paper is to present a novel approach to the Gaussian mixture background modeling model (GMM) that we call the median mixture model (MMM). The proposed method is based on the same principles as the GMM, but all of the background model parameters are estimated in a much more efficient way resulting in accelerating the algorithm by about 25% without deteriorating the modeling r...

The majority of state-of-the-art speaker recognition systems (SR) utilize speaker models that are derived from an adapted universal background model (UBM) in the form of a Gaussian mixture model (GMM). This is true for GMM supervector systems, joint factor analysis systems, and most recently i-vector systems. In all of these systems, the posterior probabilities and sufficient statistics calcula...

Object segmentation is a challenging task in image processing and computer vision. In this paper, we present a visual attention based segmentation method to segment small sized interesting objects in natural images. Different from the traditional methods, we first search the region of interest by using our novel saliency-based method, which is mainly based on band-pass filtering, to obtain the ...

In this paper, we propose a new tracking method that uses Gaussian Mixture Model (GMM) and Optical Flow approach for object tracking. The GMM approach consists of three different Gaussian distributions, the average, standard deviation and weight respectively. There are two important steps to establish the background for model, and background updates which separate the foreground and background....

This paper provides an overview of Gaussian Mixture Model (GMM) and its component of speech signal. During the earlier period it has been revealed that Gaussian Mixture Model is very much appropriate for voice modeling in speaker recognition system. For Speaker recognition, Gaussian mixture model is an essential appliance of statistical clustering. The task effortlessly performed by humans is n...

This paper describes the voice conversion based on the Mixtures of Factor Analyzers (MFA) which can provide an efficient modeling with a limited amount of training data. As a typical spectral conversion method, a mapping algorithm based on the Gaussian Mixture Model (GMM) has been proposed. In this method two kinds of covariance matrix structures are often used : the diagonal and full covarianc...

The Gaussian mixture model (GMM) has been widely used in pattern recognition problems for clustering and probability density estimation. For pattern classification, however, the GMM has to consider two issues: model structure in high-dimensional space and discriminative training for optimizing the decision boundary. In this paper, we propose a classification method using subspace GMM density mo...