  The "cocktail party" problem of fully separating multiple sources from a
single channel audio waveform remains unsolved. Current biological
understanding of neural encoding suggests that phase information is preserved
and utilized at every stage of the auditory pathway. However, current
computational approaches primarily discard phase information in order to mask
amplitude spectrograms of sound. In this paper, we seek to address whether
preserving phase information in spectral representations of sound provides
better results in monaural separation of vocals from a musical track by using a
neurally plausible sparse generative model. Our results demonstrate that
preserving phase information reduces artifacts in the separated tracks, as
quantified by the signal to artifact ratio (GSAR). Furthermore, our proposed
method achieves state-of-the-art performance for source separation, as
quantified by a mean signal to interference ratio (GSIR) of 19.46.
