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From the 1 of 7 linked papers with an AI index.

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7 papers

eess.AS2026

Autoregressive Guidance of Deep Spatially Selective Filters using Bayesian Tracking for Efficient Extraction of Moving Speakers

Jakob Kienegger, Timo Gerkmann

The paper proposes lightweight Bayesian tracking methods that use the enhanced speech signal to autoregressively guide deep spatially selective filters, enabling real‑time enhancem…

eess.AS2026

Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics

Jakob Kienegger, Tal Peer, Sina Khanagha +1

Linear spatial filters (beamformers) enable robust, generalizable and interpretable speech enhancement with performance guarantees under ideal parameterization. Modern beamformers…

eess.AS2026

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…

eess.AS2025

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…

eess.AS2025

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…

eess.AS2025

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…