Supervised Monaural Speech Enhancement Using Complementary Joint Sparse Representations

Abstract

Sparse representation is one of the most well-known methods that are applied to monaural speech enhancement. In order to make full use of the relationships among speech, noise, and mixture in sparse representation for speech enhancement, this letter proposes a novel sparsity model that consists of a couple of joint sparse representations (JSRs). One JSR uses the mapping relationship between mixture and speech while the other uses that between mixture and noise. Both relationships are used to constrain the joint dictionary learning, which effectively solves the source confusion problem of traditional methods. Moreover, the latter JSR can be complementary to the former JSR, depending on the level of structure of the noise. Thus, we propose a Gini index based weighting parameter to take their complementary advantages. The experimental results show that the proposed method outperforms state-of-the-art methods using various objective measures.