A subspace speech enhancement algorithm based on Gaussian-Laplacian-Gamma statistical models and masking properties of human ears

Subject to the subspace selection problem and the noise and Lagrange multipliers estimation problems,the Gaussian,Laplacian and Gamma models are used to describe the distribution of the speech.A subspace selection method is proposed by maximizing the speech presence probability.In the noise subspace,the conditional probability is used to estimate the noise power spectrum.To balance the noise reduction and speech distortion,Lagrange multipliers are appropriately estimated based on the masking properties of human ears.Experiments show that,the proposed subspace method produces impressive results in terms of quality measures of the enhanced speech.The proposed algorithm more effectively reduces the noise and makes the enhanced speech have better qualities.