Abstract
Most of the existing algorithms for blind sources separation have a limitation that sources are statistically independent. However, in many practical applications, the source signals are nonnegative and mutual statistically dependent signals. When the observations are nonnegative linear combinations of nonnegative sources, the correlation coefficients of the observations are larger than these of source signals. In this letter, a novel Nonnegative Matrix Factorization (NMF) algorithm with least correlated component constraints to blind separation of convolutive mixed sources is proposed. The algorithm relaxes the source independence assumption and has low-complexity algebraic computations. Simulation results on blind source separation including real face image data indicate that the sources can be successfully recovered with the algorithm.
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Supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20060280003) and Shanghai Leading Academic Discipline Project (T0102).
Communication author: Fang Yong, born in 1964, male, Ph.D., Professor.
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Zhang, Y., Fang, Y. An NMF algorithm for blind separation of convolutive mixed source signals with least correlation constrains. J. Electron.(China) 26, 557–563 (2009). https://doi.org/10.1007/s11767-008-0140-6
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DOI: https://doi.org/10.1007/s11767-008-0140-6