1 citations · 4 across the 6 of their papers we have counts for
6 papers
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms
Joseph Konan, Ojas Bhargave, Shikhar Agnihotri +12
In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (V…
PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech Enhancement
Muqiao Yang, Joseph Konan, David Bick +5
Despite rapid advancement in recent years, current speech enhancement models often produce speech that differs in perceptual quality from real clean speech. We propose a learning o…
TAPLoss: A Temporal Acoustic Parameter Loss for Speech Enhancement
Yunyang Zeng, Joseph Konan, Shuo Han +5
Speech enhancement models have greatly progressed in recent years, but still show limits in perceptual quality of their speech outputs. We propose an objective for perceptual quali…
Speech Enhancement for Virtual Meetings on Cellular Networks
Hojeong Lee, Minseon Gwak, Kawon Lee +3
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 spee…
Cellular Network Speech Enhancement: Removing Background and Transmission Noise
Amanda Shu, Hamza Khalid, Haohui Liu +3
The primary objective of speech enhancement is to reduce background noise while preserving the target's speech. A common dilemma occurs when a speaker is confined to a noisy enviro…
Improving Speech Enhancement through Fine-Grained Speech Characteristics
Muqiao Yang, Joseph Konan, David Bick +3
While deep learning based speech enhancement systems have made rapid progress in improving the quality of speech signals, they can still produce outputs that contain artifacts and…