6 citations · 14 across the 6 of their papers we have counts for
5 papers · 1 filter
DiffVQE: Hybrid Diffusion Voice Quality Enhancement Under Acoustic Echo and Noise
Haljan Lugo, Ernst Seidel, Pejman Mowlaee +2
Acoustic echo and background noise pose challenges on speech enhancement in hands-free systems and speakerphones. Discriminatively trained end-to-end methods represent a powerful s…
Coded Speech Quality Measurement by a Non-Intrusive PESQ-DNN
Ziyi Xu, Ziyue Zhao, Tim Fingscheidt
Wideband codecs such as AMR-WB or EVS are widely used in (mobile) speech communication. Evaluation of coded speech quality is often performed subjectively by an absolute category r…
Components Loss for Neural Networks in Mask-Based Speech Enhancement
Ziyi Xu, Samy Elshamy, Ziyue Zhao +1
Estimating time-frequency domain masks for single-channel speech enhancement using deep learning methods has recently become a popular research field with promising results. In thi…
A Perceptual Weighting Filter Loss for DNN Training in Speech Enhancement
Ziyue Zhao, Samy Elshamy, Tim Fingscheidt
Single-channel speech enhancement with deep neural networks (DNNs) has shown promising performance and is thus intensively being studied. In this paper, instead of applying the mea…
Convolutional Neural Networks to Enhance Coded Speech
Ziyue Zhao, Huijun Liu, Tim Fingscheidt
Enhancing coded speech suffering from far-end acoustic background noise, quantization noise, and potentially transmission errors, is a challenging task. In this work we propose two…