1 code implementation • 11 Sep 2023 • Daniel Haider, Vincent Lostanlen, Martin Ehler, Peter Balazs
Numerical simulations align with our theory and suggest that the condition number of a convolutional layer follows a logarithmic scaling law between the number and length of the filters, which is reminiscent of discrete wavelet bases.
2 code implementations • 25 Jul 2023 • Vincent Lostanlen, Daniel Haider, Han Han, Mathieu Lagrange, Peter Balazs, Martin Ehler
Waveform-based deep learning faces a dilemma between nonparametric and parametric approaches.
1 code implementation • 18 Jul 2023 • Daniel Haider, Martin Ehler, Peter Balazs
The paper uses a frame-theoretic setting to study the injectivity of a ReLU-layer on the closed ball of $\mathbb{R}^n$ and its non-negative part.
no code implementations • 24 Nov 2020 • Artemii Novoselov, Peter Balazs, Götz Bokelmann
We show that separation is possible also for seismic recordings, using techniques from machine learning (and even those recorded with a single sensor).<br />This may have an impact on seismic applications such as <br />ambient noise tomography, induced seismicity, earthquake analysis, aftershock analysis, nuclear verification, and seismoacoustics/infrasound.<br />The machine learning technique that we use for seismic signal separation is based on a dual-path recurrent neural network which is applied directly to the time domain data.
no code implementations • 22 Jul 2016 • Nathanael Perraudin, Nicki Holighaus, Piotr Majdak, Peter Balazs
We present a novel method for the compensation of long duration data loss in audio signals, in particular music.