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Found 5 papers, 2 papers with code
Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters
In a multi-channel separation task with multiple speakers, we aim to recover all individual speech signals from the mixture.
Spatially Selective Deep Non-linear Filters for Speaker Extraction
In a scenario with multiple persons talking simultaneously, the spatial characteristics of the signals are the most distinct feature for extracting the target signal.
Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement
The key advantage of using multiple microphones for speech enhancement is that spatial filtering can be used to complement the tempo-spectral processing.
On the Role of Spatial, Spectral, and Temporal Processing for DNN-based Non-linear Multi-channel Speech Enhancement
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 linear spatial filter with an independent tempo-spectral post-filter: 1) non-linear spatial filtering allows to overcome potential restrictions originating from a linear processing model and 2) joint processing of spatial and tempo-spectral information allows to exploit interdependencies between different sources of information.
Nonlinear Spatial Filtering in Multichannel Speech Enhancement
Rather, the MMSE optimal filter is a joint spatial and spectral nonlinear function.
