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Prof. Dr. Emily King

Ehemalige Leiterin der AAG Computational Data Analysis (2014-2019)

Veranstaltungen (Auswahl)vollständige Liste

  1. Algebraic, Geometric and Combinatorial Methods in Frame Theory (Sommersemester 2019)
  2. Inverse Methods and Data Analysis in Environmental Physics (Wintersemester 2018/2019)
  3. Harmonic Analysis: Theory and Applications (Wintersemester 2018/2019)
  4. Reading Course in Randomisation approaches in data analysis (Wintersemester 2017/2018)
  5. Data analysis and image processing (T3) (Wintersemester 2016/2017)

Abschlussarbeiten (Auswahl)vollständige Liste

  1. Randomized Image Decomposition and Reconstruction (Lennart Abels)
  2. Tangent and Curvature Estimation of 2D Point Clouds (Laura Breitkopf)
  3. Distributed Kalman Filtering for Large-Scale Dynamic Systems with Sparsely Coupled States (Lukas Zumvorde)
  4. Mathematical Analysis of Information Loss and Errors in Neural Networks (Sören Dittmer)
  5. Spectogram-based Musical Instrument Separation via Pitch-invariant Dictionaries (Sören Schulze)

Publikationen (Auswahl)vollständige Liste

  1. S. Schulze, E. King.
    Formulating Beurling LASSO for Source Separation via Proximal Gradient Iteration.
    Zur Veröffentlichung eingereicht.

    online unter: https://doi.org/10.48550/arXiv.2202.08082

  2. S. Schulze, E. King.
    Sparse Pursuit and Dictionary Learning for Blind Source Separation in Polyphonic Music Recordings.
    EURASIP Journal on Audio, Speech, and Music Processing, 6:2-25, 2021.

    DOI: 10.1186/s13636-020-00190-4
    online unter: https://rdcu.be/ceo1J

  3. S. Schulze, J. Leuschner, E. King.
    Blind Source Separation in Polyphonic Music Recordings Using Deep Neural Networks Trained via Policy Gradients.
    MDPI Open Access Journals Signals, 2(4):637-661, 2021.

    DOI: 10.3390/signals2040039
    online unter: https://www.mdpi.com/2624-6120/2/4/39

  4. S. Schulze, E. King.
    A Frequency‐Uniform and Pitch‐Invariant Time‐Frequency Representation.
    90th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM), 18.02.-22.02.2019, Wien, Österreich.
    Proc. Appl. Math. Mech., 19(1):e201900374, 2019.

    DOI: 10.1002/pamm.201900374

  5. S. Dittmer, E. King, P. Maaß.
    Singular values for ReLU layers.
    IEEE Transactions on Neural Networks and Learning Systems, Article , 2019.

    online unter: https://ieeexplore.ieee.org/document/8891761