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

Xiangying Lu, Bin Gao, Li Chin Khor, Wai Lok Woo, Satnam Dlay, Wingkuen Ling, Cheng S. Chin
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
more » ... 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. , the proposed separation strategy utilizes filterbank to make the observed mixed signal analyzed in sub-band domain. The impetus behind this is that the degree of mixing of the sources in the sub-band domain is now less ambiguous and thus, the dominating source in the sub-band mixture can be easily detected. Therefore, the spectral and temporal patterns (i.e. the spectral bases and temporal codes, respectively) associated in each sub-band can be extracted more accurately by using
doi:10.4236/jsip.2013.42026 fatcat:nr4aolj3ajgtrc5r4lq7fqnz3e