most citedA Secure and Efficient Federated Learning Framework for NLP

6 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SD2023

Psychoacoustic Challenges Of Speech Enhancement On VoIP Platforms

Joseph Konan, Shikhar Agnihotri, Ojas Bhargave +4

Within the ambit of VoIP (Voice over Internet Protocol) telecommunications, the complexities introduced by acoustic transformations merit rigorous analysis. This research, rooted i…

q-fin.PM2023

Optimizing Investment Strategies with Lazy Factor and Probability Weighting: A Price Portfolio Forecasting and Mean-Variance Model with Transaction Costs Approach

Shuo Han, Yinan Chen, Jiacheng Liu

Market traders often engage in the frequent transaction of volatile assets to optimize their total return. In this study, we introduce a novel investment strategy model, anchored o…

cs.SD2023★ 1 cited

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…

cs.SD2023

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…

cs.CL2023★ 1 cited

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…

cs.CR2022★ 6 cited

A Secure and Efficient Federated Learning Framework for NLP

Jieren Deng, Chenghong Wang, Xianrui Meng +7

In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks. Existing solutions either involve a trusted aggregator or require heavyw…