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

Speech Enhancement for Virtual Meetings on Cellular Networks