Senior Research Associate
Mathematics and Signal Processing in Acoustics
Machine Learning

Tel. +43 1 51581-2532
Email: nicki.holighaus(at)oeaw.ac.at

Scientific IDs:
ORCID: 0000-0003-3837-2865
Google Scholar: Nicki Holighaus
ResearchGate: researchgate.net/profile/Nicki_Holighaus

 

Academic Background


Nicki Holighaus studied mathematics and theoretical computer sciences at Justus–Liebig–University, Gießen, Germany. He graduated in 2010. After three years of doctoral studies at the University of Vienna, Austria, where he worked as a research assistant at the Numerical Harmonic Analysis Group (NuHAG), he successfully defended his PhD thesis "Theory and implementation of adaptive time-frequency transforms” in October 2013.

Since August 2012 he is part of the Acoustic Research Institute's workgroup "Mathematics and Signal Processing in Acoustics", where he works on theoretical and applied aspects of frames and adapted time-frequency representations. 

Current Research


His research focuses on advanced time-frequency methods in signal processing, including time-frequency analysis, the mathematical theory and design of adaptive and adapted time-frequency representations, time-frequency processing in acoustics and the use of time-frequency representations in machine learning for acoustics.  

Current research projects: MERLIN

Current topics:

  • Theory and application of warped time-frequency representations 
  • Function spaces and discretization for structured continuous frames
  • Structure of time-frequency phase
  • Signal processing with time-frequency phase
  • Deep learning with time-frequency features
  • Neural audio generation
  • Audio inpainting with generative neural networks
  • Time-frequency processing and perception

Publications

Publications

  • Holighaus N.; Koliander G.; Hollomey C.; Pillichshammer F. (2023) Grid-Based Decimation for Wavelet Transforms With Stably Invertible Implementation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Bd. 31, S. 789-801.
  • Hollomey C.; Holighaus N.; Koliander G. (2022) Grid-like wavelet sampling for audio processing applications. Proceedings: A16, Numerical, Computational and Theoretical Acoustics, ICA 2022. Gyeongju S. 208-214.
  • Hollomey C.; Holighaus N.; Balazs P. (2022) Music signal analysis in the Large Time Frequency Analysis Toolbox. Proceedings: A07, Musical Acoustics, ICA 2022. Gyeongju S. 98-104.
  • Balazs P.; Tauböck G.; Rajbamshi S.; Holighaus N. (2022) Audio Inpainting. Proceedings: A16, Numerical, Computational and Theoretical Acoustics, ICA 2022. Gyeongju S. 186-189.
  • Holighaus N.; Voigtlaender F. (2021) Schur-type Banach modules of integral kernels acting on mixed-norm Lebesgue spaces. Journal of Functional Analysis.
  • Průša Z.; Holighaus N.; Balazs P. (2021) Fast Matching Pursuit with Multi-Gabor Dictionaries. Transactions on Mathematical Software, Bd. 47, S. 1-20.
  • Haider D.; Holighaus N.; Balazs P. (2021) Phase-Based Signal Representations for Scattering. European Signal Processing Conference (EUSIPCO21).
  • Haider D.; Balazs P.; Holighaus N.; Gutscher L. (2021) Zeit-Frequenz Darstellungen und Deep Learning. DAGA 2021, Jahrestagung für Akustik. Vienna.
  • Marafioti A.; Holighaus N.; Majdak P. (2021) Time-Frequency Phase Retrieval for Audio - The Effect of Transform Parameters. .
  • Rajbamshi S.; Tauböck G.; Holighaus N.; Balazs P. (2021) Audio Inpainting via L1-Minimization and Dictionary Learning. European Signal Processing Conference (EUSIPCO 2021).
  • Holighaus N.; Wiesmeyr C.; Průša Z. (2020) A Class of Warped Filter Bank Frames Tailored to Non-linear Frequency Scales. Journal of Fourier Analysis and Applications, Bd. 26/1, S. 22.
  • Balazs P.; Holighaus N. (2020) LTFAT - Die Zeit-Frequenz Toolbox. Jubiläumstagungsband DAGA 2020.
  • Marafioti A.; Majdak P.; Holighaus N.; Perraudin N. (2020) GACELA - A generative adversarial context encoder for long audio inpainting. IEEE Journal of Selected Topics in Signal Processing, S. 120-131.
  • Průša Z.; Holighaus N.; Balazs P. (2020) Accelerating Matching Pursuit with multiple Time-Frequency Dictionaries. Proceedings of the 23rd International Conference on Digital Audio Effects (DAFx2020).
  • Marafioti A.; Holighaus N.; Majdak P.; Perraudin N. (2019) Audio inpainting of music by means of neural networks. 146th Convention of the Audio Engineering Society. Dublin.
  • Holighaus N.; Koliander G.; Pr uša Z.; Abreu L. D. (2019) Characterization of Analytic Wavelet Transforms and a New Phaseless Reconstruction Algorithm. IEEE Transactions on Signal Processing, Bd. 67, S. 3894-3908.
  • Holighaus N.; Koliander G.; Pr uša Z.; Abreu L. D. (2019) Non-iterative phaseless reconstruction from wavelet transform magnitude. Proceedings of the DAFx19.
  • Marafioti A.; Perraudin N.; Holighaus N.; Majdak P. (2019) Adversarial Generation of Time-Frequency Features with application in audio synthesis. Proceedings of the 36th International Conference on Machine LearningProceedings of Machine Learning Research (K. Chaudhuri and Salakhutdinov, R., eds.). PMLR, Long Beach, California, USA Bd. 97 S. 4352-4362.
  • Marafioti A.; Perraudin N.; Holighaus N.; Majdak P. (2019) A context encoder for audio inpainting. . Bd. 27 Issue 12 S. 2362 - 2372.
  • Rajbamshi S.; Balazs P.; Holighaus N. (2019) Adhoc method to Invert the Reassigned Time-Frequency Representation. Proceedings of the 23rd International Congress on Acoustics. S. 2789 - 2796.