4 citations · 4 across the 3 of their papers we have counts for
5 papers
Does a PESQNet (Loss) Require a Clean Reference Input? The Original PESQ Does, But ACR Listening Tests Don't
Ziyi Xu, Maximilian Strake, Tim Fingscheidt
Perceptual evaluation of speech quality (PESQ) requires a clean speech reference as input, but predicts the results from (reference-free) absolute category rating (ACR) tests. In t…
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet
Ziyi Xu, Maximilian Strake, Tim Fingscheidt
Speech enhancement employing deep neural networks (DNNs) for denoising are called deep noise suppression (DNS). During training, DNS methods are typically trained with mean squared…
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data
Ziyi Xu, Maximilian Strake, Tim Fingscheidt
Data-driven speech enhancement employing deep neural networks (DNNs) can provide state-of-the-art performance even in the presence of non-stationary noise. During the training proc…
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…
Concatenated Identical DNN (CI-DNN) to Reduce Noise-Type Dependence in DNN-Based Speech Enhancement
Ziyi Xu, Maximilian Strake, Tim Fingscheidt
Estimating time-frequency domain masks for speech enhancement using deep learning approaches has recently become a popular field of research. In this paper, we propose a mask-based…