Mathematics and Signal Processing in Acoustics
Tel. +43 1 51581-2545
Daniel Haider studied mathematics at the University of Vienna with an emphasis on applied mathematics and scientific computing. 2019 he wrote his master thesis entitled "Aspects of Time-Frequency Scattering and Towards Phase Scattering" under the supervision of Peter Balazs. Since 2020 he is PhD student at ARI.
Since 2019 Daniel is a member of the workgroups "Mathematics and Signal Processing in Acoustics" and "Machine Learning in Acoustics" and he is interested in approaching machine learning concepts and its application on audio with tools from abstract mathematics. In the scope of his PhD he studies techniques and methods used in artificial neural networks, employing the theory of frames.
- Haider D.; Ehler M.; Balazs P. (2023) Convex Geometry of ReLU-Layers, Injectivity on the Ball and Local Reconstruction. Proceedings of the 40th International Conference on Machine Learning. Honolulu.
- Feichtinger H.G.; Balazs P.; Haider D. (2023) Double Preconditioning for Gabor Frame Operators: Algebraic, Functional Analytic and Numerical Aspects. Applied and Computational Harmonic Analysis, Bd. 66, S. 101-137.
- Haider D.; Balazs P.; Holighaus N. (2021) Phase-based Signal Representations for Scattering. Proceedings of the 29th European Signal Processing Conference. Dublin.
- Haider D.; Balazs P.; Holighaus N.; Gutscher L. (2021) Zeit-Frequenz Darstellungen und Deep Learning. 47.Jahrestagung für Akustik. Wien.
- Haider D.; Balazs P. (2019) Extraction of Rhythmical Features with the Gabor Scattering Transform. Proceedings of the 14th International Symposium on Computer Music Multidisciplinary Research (CMMR), Marseille, France, Oct. 14-18. S. 916-923.
- Haider D. (2019) Aspects of Time-Frequency Scattering and Introducing Phase Scattering. . Universität Wien, .