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Machine Learning Source Separation Using Maximum A Posteriori Nonnegative Matrix Factorization

Lookup NU author(s): Dr Bin Gao, Dr Wai Lok Woo

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Abstract

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.

Publication metadata

Author(s): Gao B, Woo WL, Ling BWK

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Cybernetics

Year: 2014

Volume: 44

Issue: 7

Pages: 1169-1179

Print publication date: 01/07/2014

Online publication date: 08/11/2013

ISSN (print): 2168-2267

ISSN (electronic): 2168-2275

Publisher: IEEE

URL: http://dx.doi.org/10.1109/TCYB.2013.2281332

DOI: 10.1109/TCYB.2013.2281332

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