Underdetermined blind separation of source using lp-norm diversity measures

Blind separation of sources (BSS) is to recover the source signals from the observed mixture signals with no knowledge on the mixing channel. Recently, there has been more and more attention to underdetermined BSS. In the study of underdetermined BSS, it is a challenging problem to separate the source signals efficiently while without knowing the number of sources. On the one hand, the number of sources is unknown in practice. On the other hand, most traditional blind separation algorithms encounter highly computational complexity leading to poor separation performance. To remedy the shortcomings of traditional algorithms, in this paper, a novel algorithm based on p-norm-like (l((0< p <= 1))) diversity measures is proposed to solve the underdetermined BSS problem. First of all, we propose an improved information theory criteria method to detect the number of sources in the underdetermined case. Meanwhile, we use a fourth-order tensor blind identification method for the estimation of the mixing matrix. In the stage of source signal reconstruction, we develop an (l((0< p <= 1)))-norm diversity measure for better source signal reconstruction and the computational complexity is reduced significantly. Simulation results and experimental measurements demonstrate that the proposed algorithm can obtain better separation performance and achieve fast running speed. (C) 2020 Elsevier B.V. All rights reserved.