  Speech enhancement is a crucial task for several applications. Among the most
explored techniques are the Wiener filter and the LogMMSE, but approaches
exploring deep learning adapted to this task, such as SEGAN, have presented
relevant results. This study compared the performance of the mentioned
techniques in 85 noise conditions regarding quality, intelligibility, and
distortion; and concluded that classical techniques continue to exhibit
superior results for most scenarios, but, in severe noise scenarios, SEGAN
performed better and with lower variance.
