common underdetermined case, where there are fewer mixtures than sources, sparse representations of the sources are usually employed to increase the likelihood of sources being disjointed [3]. The underlying principle of all existing ICA methods is to achieve mutual independence among separator outputs. The relative success of this approach is mainly due to the convenience of the corresponding mathematical setting, provided by the independence assumption, for algorithm development. Another important factor is the applicability of the independence assumption to a relatively large subset of BSS application domains. However, the ICA-based separation schemes require large amounts of data recorded in a stationary acoustic condition to provide a reasonable estimate of model parameters. In addition, they impose a permutation problem due to misalignment of the individual source components [12; 13; 14]. The second group of separation methods is based on adaptive algorithms that optimize a multichannel filter structure according to the signal properties. In other words, an alternative geometric demixing strategy is derived based on the capability of a microphone array for directional acquisition or beamforming. This procedure achieves source separation by steering the beam pattern of the microphone array towards the desired source, thus filtering out the interferences regardless of their signal nature [15; 16]. The underlying hypothesis is that the sources are uncorrelated; this assumption is vulnerable to reverberation so the beamformer can mitigate or cancel the desired signal in acoustic reverberation. An additional limiting factor is the spatial resolution for resolving closely located sources. Furthermore, unlike the ICA approach, beamforming requires precise information about the microphone array configuration and the desired source location. Recent work considers a non-linear mixture of beamformers which incorporates the sparsity of the spectrotemporal coefficients to address underdetermined demixing [17]. The application of this method is limited to anechoic mixtures and the performance is degraded due to reverberation. The other major categories of the separation techniques are based on the sparseness of speech signals in the time-frequency (TF) domain. They assume that the sources approximately meet the W-disjoint orthogonality (W-DO) [18] in the TF domain, i.e., there is at most one sound source active at a certain TF instant. As a result, they achieve separation by partitioning the TF representations of the mixtures belonging to the same speech source. Such groupings can be based on time and phase delays [18] obtained from processing spaced microphone array recordings or intensity-based direction of arrival (DOA) estimates obtained from co-located (spatial) microphone recordings and using microphone directivity. The principle virtue of the W-DO based methods is that they are more computationally efficient compared to stochastic-based methods [19]. When the W-DO of simultaneously occurring speech signals is met, DOA estimates performed in the TF domain will correspond to the location of a true speech source. In practice, simultaneously occurring speech signals are not strictly W-DO for all TF instants, and the separated speech signals using these sparse-based approaches applied to the mixture suffer from musical and crosstalk distortion. This is a result of the non-sparse components (i.e., the TF components derived from more than one source) combining in the mixture, leading to unpredictable DOA estimates that do not correspond to true DOA estimates. The non-sparse TF component is discarded, causing musical distortion of the separated source. Further, if three frontal sources of equal energy are considered (one directly in line with the array and two at equal angles but opposite sides of the array), the non-sparse components contributed by the left and right sources may lead to the same DOA estimate as the middle source.