发表刊物：IEEE Transactions on Cybernetics
摘要：A novel unsupervised machine learning algorithm for single channel source separation (SCSS) is presented. The proposed method is based on nonnegative matrix factorization which is optimized under the framework of maximum a posteriori (MAP) probability and Itakura-Saito (IS) divergence. The method enables a generalized criterion for variable sparseness to be imposed onto the solution and prior information to be explicitly incorporated through the basis vectors. In addition, the method is scale invariant where both low and high energy components of a signal are treated with equal importance.
全部作者： W. L. Woo, Bingo W-K. Ling
页面范围：1169 – 1179