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Research
My main research area lies in the field of applied and computational harmonic analysis, audio signal processing, mathematics of machine learning (convolutional neural networks and deep learning), time-frequency analysis, sampling and approximation theory. Thereby, I am interested in both theoretical and numerical aspects of various flexible signal and image representations and in particular in the impact of new representations on real-life applications.
In this respect, I have been able to achieve results which led to methods of significant importance for the community, e.g. a flexible signal transform based on non-stationary Gabor frames.
My recent publications:
(for a complete publication list see https://nuhagphp.univie.ac.at/home/publication_doerfler.php )
[1] R. Bammer, M. Dörfler, and P. Harar. Gabor Frames and Deep Scattering Networks in Audio Processing,. Axioms, 8,(4), 2019. doi:https://doi.org/10.3390/axioms8040106.
[2] Anna Breger, Jose Ignacio Orlando, Pavol Harar, Monika Dörfler, Sophie Klimscha,Christoph Grechenig, Bianca S Gerendas, Ursula Schmidt-Erfurth, and Martin Ehler. On orthogonal projections for dimension reduction and applications in augmented target loss
functions for learning problems. Journal of Mathematical Imaging and Vision, 62:376–394, 2020. doi:https://doi.org/10.1007/s10851-019-00902-2.
[3] E. Cordero, Maurice de Gosson, M. Dörfler, and F. Nicola. Generalized Born–Jordan Distributions and Applications. Advances in Computational Mathematics, 46(51), nov 2020. doi:https://doi.org/10.1007/s10444-020-09788-w.
[4] Elena Cordero, Maurice de Gosson, Monika Dörfler, and Fabio Nicola. On the symplectic covariance and interferences of time-frequency distributions. SIAM J. Math. Anal.,
50(2):2178–2193, 2018. doi:10.1137/16M1104615.
[5] Elena Cordero, Maurice de Gosson, Monika Dörfler, and Fabio Nicola. Signal Analysis Using Born–Jordan-Type Distributions, pages 221–241. Springer International Publishing, Cham, 2021. doi:10.1007/978-3-030-69637-5_13.
[6] M. Dörfler. Learning how to Listen: Time-Frequency Analysis meets Convolutional Neural Networks. Internationale Mathematische Nachrichten, 1, March 2019. doi:10.13140/RG.2.2.28319.20641.
[7] M. Dörfler, R. Bammer, A. Breger, P. Harar, and Z. Smekal. Improving Machine Hearing on Limited Data Sets. In Proceedings of ICUMT 2019. IEEE, November 2019. doi:10.1109/icumt48472.2019.8970740.
[8] Monika Dörfler, Thomas Grill, Roswitha Bammer, and Arthur Flexer. Basic filters for convolutional neural networks applied to music: Training or design? Neural Computing and Applications, 32:941–954, 2020. doi:https://doi.org/10.1007/s00521-018-3704-x.
[9] Monika Dörfler, Franz Luef, Henry McNulty, and Eirik Skrettingland. Time-Frequency Analysis and Coorbit Spaces of Operators. arXiv preprint, 2022. doi:arxiv:2210.04844.
[10] Monika Dörfler, Franz Luef, and Eirik Skrettingland. Local structure and effective dimensionality of time series data sets. arXiv preprint3, 2021. doi:https://doi.org/10.48550/arXiv.2111.02153.
[11] Arthur Flexer, Monika Dörfler, Jan Schlüter, and Thomas Grill. Hubness as a case of technical algorithmic bias in music recommendation. In 2018 IEEE International Conference
on Data Mining Workshops (ICDMW), pages 1062–1069, 2018. doi:10.1109/ICDMW.2018.00154.
[12] Pavol Harar, Dennis Elbrächter, Monika Dörfler, and Kory D. Johnson. Redistributor: Transforming empirical data distributions. 2022. URL: https://arxiv.org/abs/
2210.14219, doi:10.48550/ARXIV.2210.14219.
[13] S. Lattner, M. Dörfler, and A. Arzt. Learning complex basis functions for invariant representations of audio. In Proceedings of ISMIR19, November 2019. doi:doi.org/10.48550/arXiv.1907.05982.2
Some selected special research achievements…
- Best Paper Award ISMIR19 (Learning Complex Basis Functions for Invariant Representations of Audio, S. Lattner; M. Dörfler; A.Arzt)
- aMOBY – Acoustic Monitoring of Biodiversity, Co-PI, WWTF
- SALSA, Interdisciplinary project with OFAI, PI, WWTF
- Audio-Miner, Interdisciplinary project with OFAI, PI, WWTF
- Hertha Firnberg-grant P21247 , PI, FWF
- Best Paper Award (Gold, DAFx-11, Constructing an invertible constant-Q transform with nonstationary Gabor frames.; G.A. Velasco, N. Holighaus, M. Dörfler, and T. Grill. )
- Co-Organisation of DAFx 2020, Vienna, Austria
- Co-Organisation of Sampta19, Bordeaux, France
- Organization of Workshop at the Erwin Schroedinger International Institute for Mathematics and Physics in Vienna. (Systematic approaches to deep learning methods for audio), 2017
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Welcome!
17th March 2023. We finally submitted our project proposal “Filter them all! Gabor theory for image quality assessment” to FWF. Keep your fingers crossed for us!