Using the CSM and VSM techniques to speed up the ICA algorithm without a loss of quality
In blind source separation problems that are implemented based on the independent component analysis (ICA) algorithm, the separation speed and quality are related inversely. In this paper, the proposed algorithms eliminate this tradeoff by generating a faster separation while maintaining the quality. In the proposed algorithms, in each frequency bin and in all of the learning steps, the separation quality of the separating matrix is compared with another one that we define as a situated matrix, and the best matrix is considered as an initial separating matrix in the next learning step. In this paper, we propose 2 algorithms based on the constant situated matrix (CSM) and the variable situated matrix (VSM). Using the simulation results, on average, the proposed CSM and VSM algorithms are about 3 and 6 times faster than the ICA algorithm, respectively, while the quality of the separated signals remains almost unchanged or becomes slightly better.
ica algorithm separation quality situated matrix