The Expectation-Maximization algorithm (EM) is widely used to find the parameters of a mixture of Gaussian probability density functions (pdfs) or briefly Gaussian components that fits the sample measurement vectors in maximum likelihood sense. In our work, the expectation-maximization (EM) algorithm for Gaussian mixture modeling is improved via three statistical tests: a) A multivariate normality test, b) a central tendency (kurtosis) criterion, and c) a test based on marginal cdf to find a discriminant to split a non-Gaussian component. Details of the method can be found in journal paper [3]. The method can be downloaded from: Download from Matlab file exchange.