61 citations · 277 across the 41 of their papers we have counts for
17 papers · 1 filter
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…
Efficient Acoustic Echo Suppression with Condition-Aware Training
Ernst Seidel, Pejman Mowlaee, Tim Fingscheidt
The topic of deep acoustic echo control (DAEC) has seen many approaches with various model topologies in recent years. Convolutional recurrent networks (CRNs), consisting of a conv…
EffCRN: An Efficient Convolutional Recurrent Network for High-Performance Speech Enhancement
Marvin Sach, Jan Franzen, Bruno Defraene +4
Fully convolutional recurrent neural networks (FCRNs) have shown state-of-the-art performance in single-channel speech enhancement. However, the number of parameters and the FLOPs/…
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…
Bandwidth-Scalable Fully Mask-Based Deep FCRN Acoustic Echo Cancellation and Postfiltering
Ernst Seidel, Rasmus Kongsgaard Olsson, Karim Haddad +3
Although today's speech communication systems support various bandwidths from narrowband to super-wideband and beyond, state-of-the art DNN methods for acoustic echo cancellation (…
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…
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…
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…
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…