  Neural networks have led to tremendous performance gains for single-task
speech enhancement, such as noise suppression and acoustic echo cancellation
(AEC). In this work, we evaluate whether it is more useful to use a single
joint or separate modules to tackle these problems. We describe different
possible implementations and give insights into their performance and
efficiency. We show that using a separate echo cancellation module and a module
for noise and residual echo removal results in less near-end speech distortion
and better performance during double-talk at same complexity.
