Abstract: The probability density distribution of one-dimention under the reverberation background can be efficiently modeled using Gaussian mixtures. EM is one of the popular algorithms for parameters estimation of Gaussian mixture probability density model. However, this method highly depends on the initial parameters. The GreedyEM algorithm can solve this problem efficiently by incrementally adding Gaussian components to the mixture. It has been used in multidimensional image manipulations. We properly simplify and apply it to learn Gaussian mixtures of onedimention reverberation successfully, present a method for determining the number of mixing components. We provide experimental results illustrating that the new algorithm can approximate the probability density curve of the receiving reverberation data, and has the potential to be used for different length of data due to its good adaptability.