Gaussian Mixture Model (GMM) with Fuzzy c-means attempts to classify signals into speech and music. Feature extraction is done before classification. The classification accuracy mainly relays on the strength of the feature extraction techniques. Simple audio features such as Time domain and Frequency domain are adopted. The time domain features are Zero Crossing Rate (ZCR) and Short Time Energy...

SUMMARY The impact of a Pseudomonas fluorescens strain, genetically modified for kanamycin resistance and lactose utilisation (the GMM), could be enhanced by soil amendment with lactose and kanamycin. Lactose addition decreased the shoot to root ratio of pea, and both soil amendments increased the populations of total culturable bacteria and the inoculated GMM. Only kanamycin perturbed the bact...

Gaussian mixture models (GMM) have been widely and successfully used in speaker recognition during the last decade. However, they are generally trained using the generative criterion of maximum likelihood estimation. In this paper, we propose a simple and efficient discriminative approach to learn GMM with a large margin criterion to solve the classification problem. Our approach is based on a ...

This paper analyzes the higher order asymptotic properties of Generalized Method of Moments (GMM) estimators for linear time series models using many lags as instruments. A data dependent moment selection method based on minimizing the approximate mean squared error is developed. In addition, a new version of the GMM estimator based on kernel weighted moment conditions is proposed. It is shown ...

Measurement error causes a downward bias when estimating a panel data linear regression model. The panel data context offers various opportunities to derive moment conditions that result in consistent GMM estimators. We consider three sources of moment conditions: (i) restrictions on the intertemporal covariance matrix of the errors in the equations, (ii) heteroskedasticity and nonlinearity in ...

Gaussian Mixture Model (GMM) is an efficient method for parametric clustering. However, traditional GMM can’t perform clustering well on data set with complex structure such as images. In this paper, kernel trick, successfully used by SVM and kernel PCA, is introduced into EM algorithm for solving parameter estimation of GMM, which is so called kernel GMM (kGMM). The basic idea of kernel GMM is...