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

Lookup NU author(s): Dr Bin Gao, Dr Li Khor, Dr Wai Lok Woo, Emeritus Professor Satnam Dlay, Professor Cheng Chin


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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.

Publication metadata

Author(s): Lu XY, Gao B, Khor LC, Woo WL, Dlay SS, Ling WK, Chin CS

Publication type: Article

Publication status: Published

Journal: Journal of Signal and Information Processing

Year: 2013

Volume: 4

Issue: 2

Pages: 186-196

Print publication date: 09/05/2013

ISSN (print): 2159-4465

ISSN (electronic): 2159-4481

Publisher: Scientific Research Publishing, Inc.


DOI: 10.4236/jsip.2013.42026


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