1 code implementation • 4 Apr 2023 • Pavol Harar, Lukas Herrmann, Philipp Grohs, David Haselbach
A key shortcoming of these supervised learning methods is their need for large training data sets, typically generated from particle models in conjunction with complex numerical forward models simulating the physics of transmission electron microscopes.
1 code implementation • 25 Oct 2022 • Pavol Harar, Dennis Elbrächter, Monika Dörfler, Kory D. Johnson
We present an algorithm and package, Redistributor, which forces a collection of scalar samples to follow a desired distribution.
no code implementations • 13 Jul 2019 • Pavol Harar, Zoltan Galaz, Jesus B. Alonso-Hernandez, Jiri Mekyska, Radim Burget, Zdenek Smekal
Automatic objective non-invasive detection of pathological voice based on computerized analysis of acoustic signals can play an important role in early diagnosis, progression tracking and even effective treatment of pathological voices.
no code implementations • 12 Jul 2019 • Pavol Harar, Jesus B. Alonso-Hernandez, Jiri Mekyska, Zoltan Galaz, Radim Burget, Zdenek Smekal
This paper describes a preliminary investigation of Voice Pathology Detection using Deep Neural Networks (DNN).
2 code implementations • 21 Mar 2019 • Pavol Harar, Roswitha Bammer, Anna Breger, Monika Dörfler, Zdenek Smekal
In this contribution we investigate how input and target representations interplay with the amount of available training data in a music information retrieval setting.
1 code implementation • 22 Jan 2019 • Anna Breger, Jose Ignacio Orlando, Pavol Harar, Monika Dörfler, Sophie Klimscha, Christoph Grechenig, Bianca S. Gerendas, Ursula Schmidt-Erfurth, Martin Ehler
In two supervised learning problems, clinical image segmentation and music information classification, the application of our proposed augmented target loss functions increase the accuracy.
2 code implementations • 27 Jun 2017 • Roswitha Bammer, Monika Dörfler, Pavol Harar
By using a simple signal model for audio signals specific properties of Gabor scattering are studied.