TY - GEN
T1 - Underdetermined blind source separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization
AU - Alshabrawy, Ossama S.
AU - Ghoneim, M. E.
AU - Awad, W. A.
AU - Hassanien, Aboul Ella
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Conventional blind source separation is based on over-determined with more sensors than sources but the underdetermined is a challenging case and more convenient to actual situation. Non-negative Matrix Factorization (NMF) has been widely applied to Blind Source Separation (BSS) problems. However, the separation results are sensitive to the initialization of parameters of NMF. Avoiding the subjectivity of choosing parameters, we used the Fuzzy C-Means (FCM) clustering technique to estimate the mixing matrix and to reduce the requirement for sparsity. Also, decreasing the constraints is regarded in this paper by using Semi-NMF. In this paper we propose a new two-step algorithm in order to solve the underdetermined blind source separation. We show how to combine the FCM clustering technique with the gradient-based NMF with the multi-layer technique. The simulation results show that our proposed algorithm can separate the source signals with high signal-to-noise ratio and quite low cost time compared with some algorithms.
AB - Conventional blind source separation is based on over-determined with more sensors than sources but the underdetermined is a challenging case and more convenient to actual situation. Non-negative Matrix Factorization (NMF) has been widely applied to Blind Source Separation (BSS) problems. However, the separation results are sensitive to the initialization of parameters of NMF. Avoiding the subjectivity of choosing parameters, we used the Fuzzy C-Means (FCM) clustering technique to estimate the mixing matrix and to reduce the requirement for sparsity. Also, decreasing the constraints is regarded in this paper by using Semi-NMF. In this paper we propose a new two-step algorithm in order to solve the underdetermined blind source separation. We show how to combine the FCM clustering technique with the gradient-based NMF with the multi-layer technique. The simulation results show that our proposed algorithm can separate the source signals with high signal-to-noise ratio and quite low cost time compared with some algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84872537379&partnerID=8YFLogxK
UR - https://fedcsis.org/2012/node/48.html
M3 - Conference contribution
AN - SCOPUS:84872537379
SN - 9781467307086
T3 - 2012 Federated Conference on Computer Science and Information Systems, FedCSIS 2012
SP - 695
EP - 700
BT - 2012 Federated Conference on Computer Science and Information Systems, FedCSIS 2012
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - 2012 Federated Conference on Computer Science and Information Systems, FedCSIS 2012
Y2 - 9 September 2012 through 12 September 2012
ER -