The Generalized State Coherence Transform (GSCT) has been recently proposed as an efficient tool for the estimation of multidimensional TDOA of multiple sources. The transform defines a multivariate likelihood of the TDOA through a non-linear integration of complex-valued states, representing the acoustic propagation of multiple sources. In the previous works the non-linearity was heuristically motivated leading to a difficult interpretation of the resulting likelihoods and of a correct choice of the parameters. Modeling the time-delays of the acoustic propagation of multiple sources with a multivariate multimodal distribution, a non-parametric kernel density estimator may be derived, which intrinsically accounts for spatial aliasing. From the theoretical analysis it follows that with an appropriate frequency-dependent non-linearity the GSCT likelihood approximates the true kernel density. Theoretical discussion is confirmed by experimental results which show that the proposed non...