6 citations · 11 across the 7 of their papers we have counts for
7 papers
Employing Real Training Data for Deep Noise Suppression
Ziyi Xu, Marvin Sach, Jan Pirklbauer +1
Most deep noise suppression (DNS) models are trained with reference-based losses requiring access to clean speech. However, sometimes an additive microphone model is insufficient f…
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…
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…