Alternating Projection Approach for Nonlinear Blind Separation of Sparse Sources
S. Akhavan, Hamid Soltanian‐Zadeh · 2021 29th European Signal Processing Conference (EUSIPCO) · 2021
In this paper, we propose an iterative method to solve the nonlinear blind source separation (BSS) problem when the sources are sparse. The main idea to solve the problem is approximation of the nonlinear mixture functions with the polynomial functions. Then, using an alternating approach, the sources and the coefficients of polynomial functions are estimated. The proposed approach is similar to one employed in dictionary learning algorithms for sparse representation. In fact, in iterations where the sources are estimated, we cluster the signals, and in iterations where the coefficients of polynomial functions are estimated, we assign a polynomial manifold to each cluster. Experimental results demonstrate the effectiveness of the proposed method relative to state-of-the-art methods.