Non-negative Matrix Factorization Speech Enhancement Method Based on Constraints of Temporal Continuity

The constrained low-rank and sparse matrix decomposition (CLSMD) method ignores the temporal continuity between adjacent speech frames in the process of speech enhancement, resulting in a sparse matrix generated by decomposition with isolated discrete points. Therefore, in order to improve the noise suppression ability of the speech system and improve the enhanced speech quality and intelligibility, this paper proposes a speech enhancement method based on Temporal continuity Constraint for Non-negative Low-rank and Sparse Matrix Decomposition (TCNLSMD). In this method, in addition to adding low -rank and sparse constraints, temporal continuity constraints are added. The proposed method based on the sparse matrix obtained by eigenvalue decomposition of non-negative matrices and hard-threshold function estimation, the discrete sparse matrix is reduced by adding temporal continuity constraints to reduce discrete isolated points, retaining more speech information and reducing the enhanced speech distortion. The experimental results show that under various types of noise test conditions, compared with the current mainstream speech enhancement methods, especially with NLSMD, the proposed method improve the noise suppression capability, make the residual noise less, and improve the quality of the enhanced speech.