4 citations · 6 across the 15 of their papers we have counts for
4 papers · 1 filter
GRAM: Spatial general-purpose audio representations for real-world environments
Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden
Audio foundation models learn general-purpose audio representations that facilitate a wide range of downstream tasks. While the performance of these models has greatly increased fo…
GRAM: Spatial general-purpose audio representation models for real-world applications
Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden
Audio foundation models learn general-purpose audio representations that facilitate a wide range of downstream tasks. While the performance of these models has greatly increased fo…
WavJEPA: Semantic learning unlocks robust audio foundation models for raw waveforms
Goksenin Yuksel, Pierre Guetschel, Michael Tangermann +2
Learning audio representations from raw waveforms overcomes key limitations of spectrogram-based audio representation learning, such as the long latency of spectrogram computation…
Leveraging Spatial Cues from Cochlear Implant Microphones to Efficiently Enhance Speech Separation in Real-World Listening Scenes
Feyisayo Olalere, Kiki van der Heijden, Christiaan H. Stronks +3
Speech separation approaches for single-channel, dry speech mixtures have significantly improved. However, real-world spatial and reverberant acoustic environments remain challengi…