Title of Paper:Machine Learning Source Separation using Maximum A Posteriori Nonnegative Matrix Factorization

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Journal:IEEE Transactions on Cybernetics

Abstract: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.

All the Authors: W. L. Woo, Bingo W-K. Ling

Correspondence Author:Bin Gao

Discipline:Engineering

Volume:44

Issue:7

Page Number:1169 – 1179

Translation or Not:no

Date of Publication:2014-06-12