Abstract
A robust version of non-negative matrix factorization (RNMF) with generalized Kullback-Leibler divergence designed for the task of unsupervised monaural speech enhancement is proposed. RNMF tackles unsupervised speech enhancement problem through factorizing the magnitude spectrum of mixture into the sum of a non-negative sparse matrix and a non-negative low-rank matrix. The parameters of nonnegative components are estimated through minimizing the reconstruction error defined by the divergence. The closed-from updating formulae of RNMF are derived using alternating direction method of multipliers. Experimental results demonstrated that the proposed algorithm yields superior results compared with the multiplicative updates at the expense of more computational complexity.
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