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R. Kompass, “A Generalized Divergence Measure for Nonnegative Matrix Factorization,” Proceedings of the Neuroinformatics Workshop, Torun, September 2005.

has been cited by the following article:

• TITLE: Single Channel Source Separation Using Filterbank and 2D Sparse Matrix Factorization

  AUTHORS: Xiangying Lu, Bin Gao, Li Chin Khor, Wai Lok Woo, Satnam Dlay, Wingkuen Ling, Cheng S. Chin

  KEYWORDS: Blind Source Separation; Non-Negative Matrix Factorization; Filterbank Analysis

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.4 No.2, May 9, 2013

  ABSTRACT: We present a novel approach to solve the problem of single channel source separation (SCSS) based on filterbank technique and sparse non-negative matrix two dimensional deconvolution (SNMF2D). The proposed approach does not require training information of the sources and therefore, it is highly suited for practicality of SCSS. The major problem of most existing SCSS algorithms lies in their inability to resolve the mixing ambiguity in the single channel observation. Our proposed approach tackles this difficult problem by using filterbank which decomposes the mixed signal into sub-band domain. This will result the mixture in sub-band domain to be more separable. By incorporating SNMF2D algorithm, the spectral-temporal structure of the sources can be obtained more accurately. Real time test has been conducted and it is shown that the proposed method gives high quality source separation performance.