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Single Channel Source Separation Using Filterbank and 2D Sparse Matrix Factorization
School of Electrical and Electronic Engineering, Newcastle University, England, UK
School of Electrical and Electronic Engineering, Newcastle University, England, UK
School of Electrical and Electronic Engineering, Newcastle University, England, UK
School of Electrical and Electronic Engineering, Newcastle University, England, UK
School of Electrical and Electronic Engineering, Newcastle University, England, UK
Faculty of Information Engineering, Guangdong University of Technology, Guangzhou, China
School of Marine Science and Technology, Newcastle University, England, UK.
- 1 School of Electrical and Electronic Engineering, Newcastle University, England, UK
- 2 School of Electrical and Electronic Engineering, Newcastle University, England, UK
- 3 School of Electrical and Electronic Engineering, Newcastle University, England, UK
- 4 School of Electrical and Electronic Engineering, Newcastle University, England, UK
- 5 School of Electrical and Electronic Engineering, Newcastle University, England, UK
- 6 Faculty of Information Engineering, Guangdong University of Technology, Guangzhou, China
- 7 School of Marine Science and Technology, Newcastle University, England, UK.
Journal of Signal and Information Processing·Volume 04 (2013)·Pages 186–196·Published 8 May 2013·DOI10.4236/jsip.2013.42026
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Abstract
We present a novel approach to solve the problem of single channel source separation (SCSS) based on filterbank tech nique 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 observa tion. 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 con ducted and it is shown that the proposed method gives high quality source separation performance.
KeywordsBlind Source SeparationNon-Negative Matrix FactorizationFilterbank Analysis
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