4 papers
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/…
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…
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…