TY - GEN
T1 - Underdetermined blind separation of an unknown number of sources based on Fourier transform and Matrix Factorization
AU - Alshabrawy, Ossama S.
AU - Ghoneim, Mohamed E.
AU - Salama, A. A.
AU - Hassanien, Aboul Ella
PY - 2013/12/1
Y1 - 2013/12/1
N2 - This paper presents an approach for underdetermined blind source separation that can be applied even if the number of sources is unknown. Moreover, the proposed approach is applicable in the case of separating I3 sources from I mixtures without additive noise. This situation is more challenging and suitable to practical real world problems. Also, the sparsity conditions are not imposed unlike to those employed by some conventional approaches. Firstly, the number of source signals are estimated followed by the estimation of the mixing matrix based on the use of short time Fourier transform and rough-fuzzy clustering. Then, source signals are normalized and recovered using modified Lin's projected gradient algorithm with modified Armijo rule. The simulation results show that the proposed approach can separate I+3 source signals from I mixed signals, and it has superior evaluation performance compared to conventional approaches.
AB - This paper presents an approach for underdetermined blind source separation that can be applied even if the number of sources is unknown. Moreover, the proposed approach is applicable in the case of separating I3 sources from I mixtures without additive noise. This situation is more challenging and suitable to practical real world problems. Also, the sparsity conditions are not imposed unlike to those employed by some conventional approaches. Firstly, the number of source signals are estimated followed by the estimation of the mixing matrix based on the use of short time Fourier transform and rough-fuzzy clustering. Then, source signals are normalized and recovered using modified Lin's projected gradient algorithm with modified Armijo rule. The simulation results show that the proposed approach can separate I+3 source signals from I mixed signals, and it has superior evaluation performance compared to conventional approaches.
KW - Armijo rule
KW - Lin's Projected Gradient
KW - Rough Fuzzy clustering
KW - Short Time Fourier transform
KW - Underdetermined Blind Source Separation
UR - http://www.scopus.com/inward/record.url?scp=84892507074&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84892507074
SN - 9781467344715
T3 - 2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013
SP - 19
EP - 25
BT - 2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013
A2 - Ganzha, Maria
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - 2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013
Y2 - 8 September 2013 through 11 September 2013
ER -