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Single Channel Source Separation Using Filterbank and 2D Sparse Matrix Factorization
2013
Journal of Signal and Information Processing
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
doi:10.4236/jsip.2013.42026
fatcat:nr4aolj3ajgtrc5r4lq7fqnz3e