Machine Learning Source Separation Using Maximum a Posteriori Nonnegative Matrix Factorization

Rong Bin Gao, Wai Lok Woo, Bingo W-k Ling

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)
2 Downloads (Pure)


A novel unsupervised machine learning algorithm for single channel source separation is presented. The proposed method is based on nonnegative matrix factorization, which is optimized under the framework of maximum a posteriori probability and Itakura-Saito 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. The proposed algorithm is a more complete and efficient approach for matrix factorization of signals that exhibit temporal dependency of the frequency patterns. Experimental tests have been conducted and compared with other algorithms to verify the efficiency of the proposed method.
Original languageEnglish
Pages (from-to)1169-1179
Number of pages11
JournalIEEE Transactions on Cybernetics
Issue number7
Early online date8 Nov 2013
Publication statusPublished - 1 Jul 2014


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