Machine Learning Source Separation Using Maximum a Posteriori Nonnegative Matrix Factorization

Gao, Bin, Woo, Wai Lok and Ling, Bingo W-K (2014) Machine Learning Source Separation Using Maximum a Posteriori Nonnegative Matrix Factorization. IEEE Transactions on Cybernetics, 44 (7). pp. 1169-1179. ISSN 2168-2267

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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.

Item Type: Article
Uncontrolled Keywords: Blind signal separation, Itakura-Saito divergence, non-negative matrix factorization, single channel, signal processing
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 26 Feb 2019 12:35
Last Modified: 01 Aug 2021 13:03

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