Speech enhancement based on sparse nonnegative matrix factorization with priors

Speech enhancement with sparse nonnegative matrix factorization and priors of noise is proposed to enhance speech contaminated by non-stationary noise. The proposed algorithm contains two steps. Firstly, the priori information about the spectrum of noise is modeled using sparse nonnegative matrix factorization algorithm and the dictionary of noise is constructed. Then, we estimate the statistical information of noise as the priors. Secondly, the spectrum of noisy speech is analyzed using sparse nonnegative matrix factorization algorithm. Then, we combine the noise dictionary, priors and iterative formulation to evaluate the dictionary and coding matrix of speech, and reconstruct the enhanced speech. Experimental results show that the proposed speech enhancement produces better speech quality than multi-band spectral subtraction, non-negative matrix factorization and non-negative sparse coding.