Speech Enhancement Using Laplacian Mixture Model under Signal Presence Uncertainty

In this paper, an estimator for speech enhancement based on Laplacian Mixture Model (LMM)has been proposed. The proposed method, estimates the complex Discerete Fourier Transfor (DFT) coefficients of clean speech from noisy speech using the Minimum Mean Square Error(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 MMSE estimator under speech presence uncertainty and the Laplacian mixture model were derived. It is shown that the proposed estimator has better performance than three estimators based on single Gaussian and single Laplacian models. Also under speech presence uncertainty the results become better.