  We study speech enhancement using deep learning (DL) for virtual meetings on
cellular devices, where transmitted speech has background noise and
transmission loss that affects speech quality. Since the Deep Noise Suppression
(DNS) Challenge dataset does not contain practical disturbance, we collect a
transmitted DNS (t-DNS) dataset using Zoom Meetings over T-Mobile network. We
select two baseline models: Demucs and FullSubNet. The Demucs is an end-to-end
model that takes time-domain inputs and outputs time-domain denoised speech,
and the FullSubNet takes time-frequency-domain inputs and outputs the energy
ratio of the target speech in the inputs. The goal of this project is to
enhance the speech transmitted over the cellular networks using deep learning
models.
