JADETD : COMBINING HIGHER-ORDER STATISTICS AND TEMPORALINFORMATION FOR BLIND SOURCE SEPARATION ( WITH NOISE )
So far blind separation algorithms have been either using information from higher order statistics or from time structure in the data. We propose the use of an error function that merges both types of information and obtain the JADE T D algorithm. This way we combine the virtues of both ingredients: the TDSEP and the JADE algorithm. We investigate the performance and usefulness of the new algorithm in several experimental scenarios. A particularly interesting scenario is the robustness of blind separation in the presence of noise, where the measuring noise eeectively doubles the number of sources and therefore leads to large separation errors for blind separation algorithms. To still allow for separation in the presence of noise, we propose to use time delayed covariance matrices for the whitening step and for the estimation of the subsequent rotation step, accordingly, also delayed cumulant information is taken into account. This technique makes use of the fact that the autocorrelation function (ACF) of the measuring noise decays much faster than the auto-correlation function of the signals.