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Prof. Dr. Emily King

Former head of AWG Computational Data Analysis (2014-2019)

Courses (Selection)complete list

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

Theses (Selection)complete list

  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)

Publications (Selection)complete list

  1. 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 at: https://www.mdpi.com/2624-6120/2/4/39

  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 at: https://rdcu.be/ceo1J

  3. 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, Vienna, Austria.
    Proc. Appl. Math. Mech., 19(1):e201900374, 2019.

    DOI: 10.1002/pamm.201900374

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

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

  5. E. King.
    2- and 3-Covariant Equiangular Tight Frames.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/1901.10612