  We present a novel approach that improves the performance of reverberant
speech separation. Our approach is based on an accurate geometric acoustic
simulator (GAS) which generates realistic room impulse responses (RIRs) by
modeling both specular and diffuse reflections. We also propose three training
methods - pre-training, multi-stage training and curriculum learning that
significantly improve separation quality in the presence of reverberation. We
also demonstrate that mixing the synthetic RIRs with a small number of real
RIRs during training enhances separation performance. We evaluate our approach
on reverberant mixtures generated from real, recorded data (in several
different room configurations) from the VOiCES dataset. Our novel approach
(curriculum learning+pre-training+multi-stage training) results in a
significant relative improvement over prior techniques based on image source
method (ISM).
