  Speech separation is the process of separating multiple speakers from an
audio recording. In this work we propose to separate the sources using a
Speaker LOcalization Guided Deflation (SLOGD) approach wherein we estimate the
sources iteratively. In each iteration we first estimate the location of the
speaker and use it to estimate a mask corresponding to the localized speaker.
The estimated source is removed from the mixture before estimating the location
and mask of the next source. Experiments are conducted on a reverberated, noisy
multichannel version of the well-studied WSJ-2MIX dataset using word error rate
(WER) as a metric. The proposed method achieves a WER of $44.2$%, a $34$%
relative improvement over the system without separation and $17$% relative
improvement over Conv-TasNet.
