5 papers · 1 filter
Adaptive Rotary Steering with Joint Autoregression for Robust Extraction of Closely Moving Speakers in Dynamic Scenarios
Jakob Kienegger, Timo Gerkmann
Latest advances in deep spatial filtering for Ambisonics demonstrate strong performance in stationary multi-speaker scenarios by rotating the sound field toward a target speaker pr…
An Analysis of Joint Nonlinear Spatial Filtering for Spatial Aliasing Reduction
Alina Mannanova, Jakob Kienegger, Timo Gerkmann
The performance of traditional linear spatial filters for speech enhancement is constrained by the physical size and number of channels of microphone arrays. For instance, for larg…
Self-Steering Deep Non-Linear Spatially Selective Filters for Efficient Extraction of Moving Speakers under Weak Guidance
Jakob Kienegger, Alina Mannanova, Huajian Fang +1
Recent works on deep non-linear spatially selective filters demonstrate exceptional enhancement performance with computationally lightweight architectures for stationary speakers o…
Steering Deep Non-Linear Spatially Selective Filters for Weakly Guided Extraction of Moving Speakers in Dynamic Scenarios
Jakob Kienegger, Timo Gerkmann
Recent speaker extraction methods using deep non-linear spatial filtering perform exceptionally well when the target direction is known and stationary. However, spatially dynamic s…
Mask-Weighted Spatial Likelihood Coding for Speaker-Independent Joint Localization and Mask Estimation
Jakob Kienegger, Alina Mannanova, Timo Gerkmann
Due to their robustness and flexibility, neural-driven beamformers are a popular choice for speech separation in challenging environments with a varying amount of simultaneous spea…