30 citations · 30 across the 10 of their papers we have counts for
Showing 2023Show all
2 papers · 1 filter
eess.AS2023
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…
eess.AS2023
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/…