Author

Timo Gerkmann

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Preprint Sep 2026

Directivity-Conditioned Low-Latency Neural Filtering for Speech Enhancement in Hearing Aids

Latest advances in neural directional filtering show exceptional results in adapting the direction and shape of directivity patterns during the inference phase. However, in the existing methods for adapting directivity patterns during inference, important real-world constraints have been disregarded. Particularly for h...

Lennart Uphaus, Andr'e Merboldt, Markus Hofbauer et al. · 0 citations

#machine learning Preprint Sep 2026

Livin'on a Prior: Likelihood Score Approximation for Inverse Problems

Generative models have found great success as data-driven methods of solving inverse problems. Two popular approaches work either by combining a pretrained generative prior with a known degradation model, or by training a conditional generative model directly from paired data. We target a setting that spans both regime...

Rostislav Makarov, Tal Peer, Danilo de Oliveira et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Test-time adaptation for speech enhancement with an autoregressive speech prior

Test-time adaptation (TTA) offers a promising direction for improving speech enhancement models under mismatched acoustic conditions, without requiring access to labeled target data. In this work, we propose a single-utterance TTA method that regularizes a pretrained speech enhancement model using an autoregressive pri...

S. Kammoun, Simon Leglaive, Xavier Alameda-Pineda et al. · 1 citation