73 citations · 116 across the 6 of their papers we have counts for
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eess.AS2022
Spatially Selective Deep Non-linear Filters for Speaker Extraction
Kristina Tesch, Timo Gerkmann
In a scenario with multiple persons talking simultaneously, the spatial characteristics of the signals are the most distinct feature for extracting the target signal. In this work,…
eess.AS2022
On the Role of Spatial, Spectral, and Temporal Processing for DNN-based Non-linear Multi-channel Speech Enhancement
Kristina Tesch, Nils-Hendrik Mohrmann, Timo Gerkmann
Employing deep neural networks (DNNs) to directly learn filters for multi-channel speech enhancement has potentially two key advantages over a traditional approach combining a line…
eess.AS2022★ 73 cited
Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement
Kristina Tesch, Timo Gerkmann
The key advantage of using multiple microphones for speech enhancement is that spatial filtering can be used to complement the tempo-spectral processing. In a traditional setting,…