A New Speech Enhancement Method Based on Nonnegative Low-rank and Sparse Decomposition

Abstract

Enhancement of speech degraded by strong noises is a highly difficult task. In this paper, a nonnegative low-rank and sparse matrix decomposition (NLSMD) based speech enhancement method is given to address this problem. The proposed method is motivated with assumptions that in time-frequency (T-F) domain, since power spectrum of many types of noise with different frame are often correlative, noise can be assumed with a low-rank structure, while speeches are often sparse in T-F units. Based on these assumptions, we formulate the speech enhancement as a NLSMD problem, and design an objective function to recover speech component. Compared with traditional methods, the NLSMD-based method does not require a speech activity detector for noise density estimation. Experimental results show the proposed method can achieve better performance over many traditional methods in strong noise conditions, in terms of yielding less residual noise and lower speech distortion.