Speech enhancement in spectral envelope and details subspaces

Supervised speech enhancement techniques have been proved to improve speech intelligibility. However, one major challenge of supervised approaches involves the overlapped spectral bases between speech and noise components in spectral dictionary space. In this study, we address this challenge through a combination strategy of spectral modulation decoupling and low-rank and sparsity oriented decomposition. Specifically, supervised low-rank and sparse decompositions with energy thresholding are developed in the spectral envelop subspace, In the spectral details subspace, an unsupervised robust principal component analysis is utilized to extract the fine structure. The validation results show that, compared with five speech enhancement algorithms, including MMSE-SPP, NMF-RPCA, RPCA, LARC and BNMF, the proposed algorithms achieves satisfactory performance on improving both perceptual quality and speech intelligibility.