Speech Enhancement for Virtual Meetings on Cellular Networks

• URL: http://arxiv.org/abs/2302.00868v1
• Date: Thu, 2 Feb 2023 04:35:48 GMT
• Title: Speech Enhancement for Virtual Meetings on Cellular Networks
• Authors: Hojeong Lee, Minseon Gwak, Kawon Lee, Minjeong Kim, Joseph Konan and Ojas Bhargave
• Abstract summary: We study speech enhancement using deep learning (DL) for virtual meetings on cellular devices. We collect a transmitted DNS (t-DNS) dataset using Zoom Meetings over T-Mobile network. The goal of this project is to enhance the speech transmitted over the cellular networks using deep learning models.
• Score: 1.487576938041254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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