FIR Convolutive BSS Based on Sparse Representation

Based on sparse representation, this paper discusses convolutive BSS of sparse sources and presents a FIR convolutive BSS algorithm that works in the frequency domain. This algorithm does not require that source signals be i.i.d or stationary, but require that source signals be sufficiently sparse in frequency domain. Furthermore, our algorithm can overcome permutation problem of frequency convolutive BSS method. For short-order FIR convolution, simulation shows good performance of our algorithm.

[1]  Michael Elad,et al.  Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Barak A. Pearlmutter,et al.  Blind Source Separation by Sparse Decomposition in a Signal Dictionary , 2001, Neural Computation.

[3]  Liu Ju A Survey of Blind Source Separation and Blind Deconvolution , 2002 .

[4]  Lucas C. Parra,et al.  Convolutive blind separation of non-stationary sources , 2000, IEEE Trans. Speech Audio Process..

[5]  Kari Torkkola,et al.  Blind separation of delayed sources based on information maximization , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[6]  Adriana Dapena,et al.  A novel frequency domain approach for separating convolutive mixtures of temporally-white signals , 2003, Digit. Signal Process..

[7]  Barak A. Pearlmutter,et al.  Blind source separation by sparse decomposition , 2000, SPIE Defense + Commercial Sensing.

[8]  Terrence J. Sejnowski,et al.  Learning Overcomplete Representations , 2000, Neural Computation.

[9]  Terrence J. Sejnowski,et al.  Blind source separation of more sources than mixtures using overcomplete representations , 1999, IEEE Signal Processing Letters.

[10]  Tan Li-l Multi-Input Multi-output (MIMO) Blind Deconvolution Via Maximum Entropy (ME) Method , 2000 .

[11]  D. Donoho,et al.  Maximal Sparsity Representation via l 1 Minimization , 2002 .

[12]  Yuanqing Li,et al.  Analysis of Sparse Representation and Blind Source Separation , 2004, Neural Computation.

[13]  Christian Jutten,et al.  Blind source separation for convolutive mixtures , 1995, Signal Process..

[14]  Michael Zibulevsky,et al.  Underdetermined blind source separation using sparse representations , 2001, Signal Process..

[15]  Mineichi Kudo,et al.  Performance analysis of minimum /spl lscr//sub 1/-norm solutions for underdetermined source separation , 2004, IEEE Transactions on Signal Processing.

[16]  Ehud Weinstein,et al.  Criteria for multichannel signal separation , 1994, IEEE Trans. Signal Process..