LAUSR

Most traditional blind source separation (BSS) methods are based on the basic assumption that source signals are mutually independent. However, many signals in the real world are often interrelated to each other. In this letter, a novel BSS method is proposed for dependent signals which only relies on the relaxed sparsity of sources. Under this constraint, the column vector of high-dimensional observed signals would be distributed in a specific subspace. According to the representation relation in time-frequency (TF) domain, each column vector of observed signal is uniquely clustered into the subspace, to which it belongs. Furthermore, the estimation of source signals is calculated by the inverse matrix or the pseudo-inverse matrix of the mixing matrix. It is also demonstrated mathematically and experimentally that under relaxed sparsity conditions, proposed subspace clustering methods is promising and hopeful to solve the dependent BSS (DBSS) problems.