73 citations · 116 across the 6 of their papers we have counts for
6 papers
Robustness of Speech Separation Models for Similar-pitch Speakers
Bunlong Lay, Sebastian Zaczek, Kristina Tesch +1
Single-channel speech separation is a crucial task for enhancing speech recognition systems in multi-speaker environments. This paper investigates the robustness of state-of-the-ar…
Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters
Kristina Tesch, Timo Gerkmann
In a multi-channel separation task with multiple speakers, we aim to recover all individual speech signals from the mixture. In contrast to single-channel approaches, which rely on…
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,…
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…
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,…
Nonlinear Spatial Filtering in Multichannel Speech Enhancement
Kristina Tesch, Timo Gerkmann
The majority of multichannel speech enhancement algorithms are two-step procedures that first apply a linear spatial filter, a so-called beamformer, and combine it with a single-ch…