1 citations · 1 across the 4 of their papers we have counts for
4 papers
BLINC: Blind Calibration For Training-Free Speech Enhancement Adaptation
Tobias Raichle, Ekaterina Gavrilko, Bin Yang
Speech enhancement (SE) models degrade under domain shifts and have to adapt to unseen target domains during deployment. Most existing test-time adaptation (TTA) methods for SE do…
Test-Time Adaptation For Speech Enhancement Via Mask Polarization
Tobias Raichle, Erfan Amini, Bin Yang
Adapting speech enhancement (SE) models to unseen environments is crucial for practical deployments, yet test-time adaptation (TTA) for SE remains largely under-explored due to a l…
Test-Time Adaptation for Speech Enhancement via Domain Invariant Embedding Transformation
Tobias Raichle, Niels Edinger, Bin Yang
Deep learning-based speech enhancement models achieve remarkable performance when test distributions match training conditions, but often degrade when deployed in unpredictable rea…
A Lost Opportunity for Vision-Language Models: A Comparative Study of Online Test-Time Adaptation for Vision-Language Models
Mario Döbler, Robert A. Marsden, Tobias Raichle +1
In deep learning, maintaining model robustness against distribution shifts is critical. This work explores a broad range of possibilities to adapt vision-language foundation models…