PSO-based learning rate adjustment for blind source separation

Blind source separation (BSS) is a technique for recovering a set of source signals without a priori information on the transformation matrix or the probability distributions of the source signals. In the previous works of BSS, the choice of the learning rate would reflect a trade-off between the stability and the speed of convergence. In this paper, we adapted the particle swarm optimization (PSO) technique to find suitable learning rates for each signal in each time slot. Experiments employing four mixed source signals were separated by our work and compared with other related approaches. The proposed approach exhibited rapid convergence and made the independent component analysis (ICA) algorithms become more efficient and stable than other related approaches.