  In this work we present a new single-microphone speech dereverberation
algorithm. First, a performance analysis is presented to interpret that
algorithms focused on improving solely magnitude or phase are not good enough.
Furthermore, we demonstrate that few objective measurements have high
correlation with the clean magnitude while others with the clean phase.
Consequently ,we propose a new architecture which consists of two sub-models,
each of which is responsible for a different task. The first model estimates
the clean magnitude given the noisy input. The enhanced magnitude together with
the noisy-input phase are then used as inputs to the second model to estimate
the real and imaginary portions of the dereverberated signal. A training scheme
including pre-training and fine-tuning is presented in the paper. We evaluate
our proposed approach using data from the REVERB challenge and compare our
results to other methods. We demonstrate consistent improvements in all
measures, which can be attributed to the improved estimates of both the
magnitude and the phase.
