Underdetermined blind source separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization

Alshabrawy, Ossama S., Ghoneim, M. E., Awad, W. A. and Hassanien, Aboul Ella (2012) Underdetermined blind source separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization. In: 2012 Federated Conference on Computer Science and Information Systems (FedCSIS), 9-12th Sept 2012, Wroclaw, Poland.

Underdetermined_Blind_Source_Separation_based_on_Fuzzy_C_Means_Clustering_and_Semi_Nonnegative_Matrix_Factorization.pdf - Accepted Version

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Official URL: https://ieeexplore.ieee.org/document/6354486


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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: blind source separation, fuzzy set theory, matrix decomposition, pattern clustering, sparse matrices, underdetermined blind source separation, seminonnegative matrix factorization, BSS problems, semiNMF parameter initialization, fuzzy c-means clustering technique, FCM clustering technique, sparsity, two-step algorithm, gradient-based NMF, multilayer technique, signal-to-noise ratio, cost time, clustering algorithms, blind source separation, Sensors, signal to noise ratio, estimation, convergence
Subjects: G400 Computer Science
G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
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Depositing User: John Coen
Date Deposited: 12 Mar 2020 09:36
Last Modified: 31 Jul 2021 19:04
URI: http://nrl.northumbria.ac.uk/id/eprint/42456

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