18 citations · 18 across the 4 of their papers we have counts for
4 papers
Approaching Domain Generalization with Embeddings for Robust Discrimination and Recognition of RF Communication Signals
Lukas Henneke, Frank Kurth
Radio frequency (RF) signal recognition plays a critical role in modern wireless communication and security applications. Deep learning-based approaches have achieved strong perfor…
Projected Belief Networks With Discriminative Alignment for Acoustic Event Classification: Rivaling State of the Art CNNs
Paul M. Baggenstoss, Kevin Wilkinghoff, Felix Govaers +1
The projected belief network (PBN) is a generative stochastic network with tractable likelihood function based on a feed-forward neural network (FFNN). The generative function oper…
Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?
Kevin Wilkinghoff, Frank Kurth
State-of-the-art anomalous sound detection systems often utilize angular margin losses to learn suitable representations of acoustic data using an auxiliary task, which usually is…
Using the Projected Belief Network at High Dimensions
Paul M Baggenstoss
The projected belief network (PBN) is a layered generative network (LGN) with tractable likelihood function, and is based on a feed-forward neural network (FFNN). There are two ver…