6 papers
AudioSphere: Towards Self-Supervised Spatial Audio Representation Models
Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden
Recent audio embedding models learn representations that transfer across clip- and frame-level tasks, yet they operate almost exclusively on single-channel signals and are blind to…
EquiSELD: Efficient training of equivariant sound event localization and detection networks
Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden
First-order Ambisonics (FOA) signals exhibit exact O(3) symmetry: The rotation or reflection of the FOA signal modifies the direction of arrival of the sound sources, while preserv…
Bearings: Self-Supervised Soundfield Embeddings from First-Order Ambisonics
Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden
Recently proposed self-supervised audio encoders learn powerful general-purpose representations of sound scenes, yet they are spatially blind. To supply the missing spatial represe…
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…
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…
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…