activity
20162026
most citedLabel Tree Embeddings for Acoustic Scene Classification

33 citations · 82 across the 8 of their papers we have counts for

collaborators

11 papers

eess.SP2026

Scattering Transform for Auditory Attention Decoding

René Pallenberg, Fabrice Katzberg, Alfred Mertins +1

The use of hearing aids will increase in the coming years due to demographic change. One open problem that remains to be solved by a new generation of hearing aids is the cocktail…

physics.med-ph2025

Efficient Chebyshev Reconstruction for the Anisotropic Equilibrium Model in Magnetic Particle Imaging

Christine Droigk, Daniel Hernández Durán, Marco Maass +2

Magnetic Particle Imaging (MPI) is a tomographic imaging modality capable of real-time, high-sensitivity mapping of superparamagnetic iron oxide nanoparticles. Model-based image re…

eess.IV2024★ 10 cited

Equilibrium Model with Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging

Marco Maass, Tobias Kluth, Christine Droigk +4

Magnetic particle imaging is a tracer-based tomographic imaging technique that allows the concentration of magnetic nanoparticles to be determined with high spatio-temporal resolut…

cs.SD2017

Audio Scene Classification with Deep Recurrent Neural Networks

Huy Phan, Philipp Koch, Fabrice Katzberg +3

We introduce in this work an efficient approach for audio scene classification using deep recurrent neural networks. An audio scene is firstly transformed into a sequence of high-l…

cs.SD2016★ 24 cited

What Makes Audio Event Detection Harder than Classification?

Huy Phan, Philipp Koch, Fabrice Katzberg +4

There is a common observation that audio event classification is easier to deal with than detection. So far, this observation has been accepted as a fact and we lack of a careful a…

cs.SD2016

Measurement of Sound Fields Using Moving Microphones

Fabrice Katzberg, Radoslaw Mazur, Marco Maass +2

The sampling of sound fields involves the measurement of spatially dependent room impulse responses, where the Nyquist-Shannon sampling theorem applies in both the temporal and spa…