61 citations · 277 across the 41 of their papers we have counts for
7 papers · 2 filters
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 Residual Echo Suppression and Noise Reduction: A Multi-Input FCRN Approach in a Hybrid Speech Enhancement System
Jan Franzen, Tim Fingscheidt
Deep neural network (DNN)-based approaches to acoustic echo cancellation (AEC) and hybrid speech enhancement systems have gained increasing attention recently, introducing signific…
Relaxed Attention: A Simple Method to Boost Performance of End-to-End Automatic Speech Recognition
Timo Lohrenz, Patrick Schwarz, Zhengyang Li +1
Recently, attention-based encoder-decoder (AED) models have shown high performance for end-to-end automatic speech recognition (ASR) across several tasks. Addressing overconfidence…
Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition
Timo Lohrenz, Zhengyang Li, Tim Fingscheidt
Stream fusion, also known as system combination, is a common technique in automatic speech recognition for traditional hybrid hidden Markov model approaches, yet mostly unexplored…
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…
AEC in a NetShell: On Target and Topology Choices for FCRN Acoustic Echo Cancellation
Jan Franzen, Ernst Seidel, Tim Fingscheidt
Acoustic echo cancellation (AEC) algorithms have a long-term steady role in signal processing, with approaches improving the performance of applications such as automotive hands-fr…
Y-Net FCRN for Acoustic Echo and Noise Suppression
Ernst Seidel, Jan Franzen, Maximilian Strake +1
In recent years, deep neural networks (DNNs) were studied as an alternative to traditional acoustic echo cancellation (AEC) algorithms. The proposed models achieved remarkable perf…