  In this work, we tackle a denoising and dereverberation problem with a
single-stage framework. Although denoising and dereverberation may be
considered two separate challenging tasks, and thus, two modules are typically
required for each task, we show that a single deep network can be shared to
solve the two problems. To this end, we propose a new masking method called
phase-aware beta-sigmoid mask (PHM), which reuses the estimated magnitude
values to estimate the clean phase by respecting the triangle inequality in the
complex domain between three signal components such as mixture, source and the
rest. Two PHMs are used to deal with direct and reverberant source, which
allows controlling the proportion of reverberation in the enhanced speech at
inference time. In addition, to improve the speech enhancement performance, we
propose a new time-domain loss function and show a reasonable performance gain
compared to MSE loss in the complex domain. Finally, to achieve a real-time
inference, an optimization strategy for U-Net is proposed which significantly
reduces the computational overhead up to 88.9% compared to the na\"ive version.

Audio and Speech Processing;Sound