The authors propose a new solution to the blind separation of sources (BSS) based on statistical independence. In the 2-dimensional case we prove that, under the whiteness constraint, the fourth-order moment based approximation of the marginal entropy (ME) cost function yields a sinusoidal objective function. Therefore, we can minimize it by simply estimating its phase. We prove that this estimator is consistent for any source distribution. In addition, such results are useful for interpreting

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... ther algorithms such as the CuBICA and the WAML (or WE). Based on the WAML, we provide a general unifying form for several previous approximations to the ME contrast. The bias and the variance of this estimator have been included. Finally, simulations illustrate the good consistency, convergence and accuracy of the proposed method. Index Terms-Array signal processing, blind source separation, higher order statistics, independent component analysis, unsupervised learning. EDICS: IDSS (inverse methods, deconvolution, source separation) J.J. Murillo-Fuentes is with the ATSC.