Design of Gaussian mixture models using matching pursuit
نویسندگان
چکیده
In this paper, a new design algorithm for estimating the parameters of Gaussian Mixture Models is presented. The method is based on the matching pursuit algorithm. Speaker Identification is considered as an application area. The estimated GMM performs as good as the EM algorithm based model. Computational complexity of the proposed method is much lower than the EM algorithm.
منابع مشابه
IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL
Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...
متن کاملImage Segmentation using Gaussian Mixture Model
Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...
متن کاملSpeech Enhancement Using Gaussian Mixture Models, Explicit Bayesian Estimation and Wiener Filtering
Gaussian Mixture Models (GMMs) of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made on the nature or stationarity of the noise. No voice activity detection (VAD) or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equatio...
متن کاملSpeech Enhancement using Laplacian Mixture Model under Signal Presence Uncertainty
In this paper an estimator for speech enhancement based on Laplacian Mixture Model has been proposed. The proposed method, estimates the complex DFT coefficients of clean speech from noisy speech using the MMSE estimator, when the clean speech DFT coefficients are supposed mixture of Laplacians and the DFT coefficients of noise are assumed zero-mean Gaussian distribution. Furthermore, the MMS...
متن کاملLamb Waves Decomposition and Mode Identification Using Matching Pursuit Method
Matching pursuit (MP) is an adaptive signal decomposition technique and can be applied to process Lamb waves, such as denoising, wave parameter estimation, and feature extraction, for health monitoring applications. This paper explored matching pursuit decomposition using Gaussian and chirplet dictionaries to decompose/approximate Lamb waves and extract wave parameters. While Gaussian dictionar...
متن کامل