Single-channel source separation using EMD-subband variable regularized sparse features

Bin Gao*, W. L. Woo, S. S. Dlay

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

122 Citations (Scopus)


A novel approach to solve the single-channel source separation (SCSS) problem is presented. Most existing supervised SCSS methods resort exclusively to the independence waveform criteria as exemplified by training the prior information before the separation process. This poses a significant limiting factor to the applicability of these methods to real problem. Our proposed method does not require training knowledge for separating the mixture and it is based on decomposing the mixture into a series of oscillatory components termed as the intrinsic mode functions (IMFs). We show, in this paper, that the IMFs have several desirable properties unique to SCSS problem and how these properties can be advantaged to relax the constraints posed by the problem. In addition, we have derived a novel sparse non-negative matrix factorization to estimate the spectral bases and temporal codes of the sources. The proposed algorithm is a more complete and efficient approach to matrix factorization where a generalized criterion for variable sparseness is imposed onto the solution. Experimental testing has been conducted to show that the proposed method gives superior performance over other existing approaches.

Original languageEnglish
Article number5570953
Pages (from-to)961-976
Number of pages16
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number4
Publication statusPublished - 13 Sept 2010


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