关键词: 语音增强, 稀疏非负矩阵分解, 交替方向乘子法

Abstract: This paper proposes a speech enhancement algorithm putting the theory of Alternating Direction Method of Multipliers（ADMM） into the algorithm of sparse nonnegative matrix factorization, which can solve the problems such as slow convergence and poor local optima in the traditional speech enhancement based Nonnegative Matrix Factorization（NMF）. It mainly consists of a training stage and an enhancement stage. During the training stage, the dictionaries of the noise are constructed as the prior information by using the ADMM based nonnegative matrix factorization. In the enhancement stage, the spectrum of noisy speech is analyzed by the sparse nonnegative matrix factorization algorithm. After that, the noise dictionary is combined with iterative formulation to evaluate the speech dictionary and the coding matrix of speech. The clean part of the speech is finally reconstructed from the noisy speech. Compared with the traditional speech enhancement methods of NMF, extensive experiments indicate that this algorithm not only has faster speed but also gets better noise suppression performance especially under instantaneous noise environment.

Key words: speech enhancement, sparse nonnegative matrix factorization, alternating direction method of multipliers