Research Scientist
Mathematics Cluster
Frame Theory and its Implementation
Speaker of Machine Learning Team

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

  • Prediction of parameters of a pinna model from synthetic geometries using a vision transformer. / Pausch, Florian; Perfler, Felix; Holighaus, Nicki et al.
    In: Journal of the Acoustical Society of America, Vol. 159, No. 6, 18.06.2026, p. 5578-5598.
    DOIRISENWBIB

    The acquisition of the human pinna geometry requires elaborate equipment for accurate results. Even then, the results are often corrupted by measurement artifacts. We introduce Mesh2PPM, a framework facilitating the generation of a personalized and artifact-free pinna mesh. Mesh2PPM predicts the parameters of a parametric pinna model based on cubic Bézier curves (BezierPPM) from a pinna mesh via a vision transformer. We evaluated Mesh2PPM with multi-view renderings of synthetic pinna geometries, providing additional depth information, varying the grids of camera views, and jittering the camera views. While added depth information had no practically relevant effect, a grid with 3 ×3 camera views facilitated the lowest overall prediction errors and best counteracted the detrimental effects of jitter. For this grid, with and without jitter, the median Pompeiu-Hausdorff distances were 1.98 mm and 1.34 mm, respectively, and the root mean square distances were 0.92 mm and 0.52 mm. A refined analysis targeting the perceptually most important pinna regions for sound localization showed that multi-view information particularly improved the prediction of BezierPPM parameters describing the cavum-conchae region. The accuracy achieved indicates the suitability of Mesh2PPM to retrieve BezierPPM parameters from pinna meshes.

  • On the crossband filter representations of LTI systems: Algorithms and inversion. / Matsuyama, Naoya; Holighaus, Nicki; Yamada, Koki et al.
    In: Acoustical Science and Technology, Vol. 47, No. 1, 01.01.2026, p. 13-23.
    DOIRISENWBIB
  • Discretization of Continuous Frames by Quasi-Monte Carlo Methods. / Zimmermann, Jan; Klotz, Andreas; Holighaus, Nicki.
    2025 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2025.
    DOIRISENWBIB
  • ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration. / Haider, Daniel; Perfler, Felix; Balazs, Peter et al.
    2025 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2025.
    DOIRISENWBIB
  • Mesh2PPM - Automatic Parametrization of the BezierPPM: Entire Pinna. / Pausch, Florian; Perfler, Felix; Holighaus, Nicki et al.
    AES Europe 2025 Conference Proceedings. Audio Engineering Society, 2025. 336.
    WebsiteRISENWBIB
  • Parametric model of the human pinna based on Bézier curves and concave deformations. / Perfler, F; Pausch, F; Pollack, K et al.
    In: Computers in Biology and Medicine, Vol. 188, 109817, 01.04.2025.
    DOIRISENWBIB
  • Robust Multicomponent Tracking of Ultrasonic Vocalizations. / Abbasi, R; Holighaus, N; Balazs, P et al.
    ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Hyderabad, 2025. p. 1-5.
    DOIRISENWBIB
  • Coorbit theory of warped time-frequency systems in R^d. / Holighaus, N; Voigtlaender, F.
    In: Journal of Fourier Analysis and Applications, Vol. 30, 25.10.2024, p. Nr. 62.
    DOIRISENWBIB
  • LTFATPY: towards making a wide range of time-frequency representations available in Python. / Hollomey, C; Holighaus, N.
    Proceedings of the 27th International Conference on Digital Audio Effects. Guildford, UK, 2024. p. 479-482.
    RISENWBIB
  • Time-frequency analysis on flat tori and Gabor frames in finite dimensions. / Abreu, L; Balazs, P; Holighaus, N et al.
    In: Applied and Computational Harmonic Analysis, Vol. 69, 01.03.2024, p. 101622.
    DOIRISENWBIB
  • Comparison of deep-neural-network architectures for the prediction of head-related transfer functions using a parametric pinna model. / Pausch, F; Perfler, F; Holighaus, N et al.
    Proceedings of the Forum Acusticum 2023. Turin, 2023.
    DOIRISENWBIB
  • Rotated time-frequency lattices are sets of stable sampling for continuous wavelet systems. / Holighaus, N; Koliander, G.
    14th International Conference on Sampling Theory and Applications. Yale, 2023.
    DownloadRISENWBIB
  • Grid-Based Decimation for Wavelet Transforms With Stably Invertible Implementation. / Holighaus, N; Koliander, G; Hollomey, C et al.
    In: IEEE/ACM Transactions on Audio Speech and Language Processing, Vol. 31, 13.01.2023, p. 789-801.
    DOIRISENWBIB
  • Grid-like wavelet sampling for audio processing applications. / Hollomey, C; Holighaus, N; Koliander, G.
    Proceedings: A16, Numerical, Computational and Theoretical Acoustics, ICA 2022. Gyeongju, 2022. p. 208-214.
    RISENWBIB
  • Music signal analysis in the Large Time Frequency Analysis Toolbox. / Hollomey, C; Holighaus, N; Balazs, P.
    Proceedings: A07, Musical Acoustics, ICA 2022. Gyeongju, 2022. p. 98-104.
    RISENWBIB
  • Audio Inpainting. / Balazs, P; Tauböck, G; Rajbamshi, S et al.
    Proceedings: A16, Numerical, Computational and Theoretical Acoustics, ICA 2022. Gyeongju, 2022. p. 186-189.
    RISENWBIB
  • Gutachten für DAFx2022_07_2022. / Holighaus, Nicki.
    2022.
    RISENWBIB
  • Fast Matching Pursuit with Multi-Gabor Dictionaries. / Průša, Z; Holighaus, N; Balazs, P.
    In: Transactions on Mathematical Software, Vol. 47, 01.12.2021, p. 1-20.
    DOIRISENWBIB
  • Zeit-Frequenz Darstellungen und Deep Learning. / Haider, D; Balazs, P; Holighaus, N et al.
    DAGA 2021, Jahrestagung für Akustik. Vienna, 2021.
    RISENWBIB
  • Audio Inpainting via L1-Minimization and Dictionary Learning. / Rajbamshi, S; Tauböck, G; Holighaus, N et al.
    European Signal Processing Conference (EUSIPCO 2021). 2021.
    RISENWBIB