Speech enhancement in spectral envelop and details subspaces

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.
Source: Speech Communication - Category: Speech-Language Pathology Source Type: research