A novel speech enhancement method based on constrained low-rank and sparse matrix decomposition
We model speech enhancement as a low-rank and sparse matrix decomposition problem. We examine the influence of low-rank and sparse constraints on performance. We find many types of noises timefrequency matrix has a low-rank structure. The increasing of sparsity can help to reduce noise in low SNR condition. The proposed method has good performance in low SNR conditions.
speech enhancement matrix decomposition low-rank matrix approximation robust principal component analysis