Reemt Hinrichs, M.Sc.
Leibniz Universität Hannover
Schneiderberg 32
30167 Hannover
phone: +49 511 762-5055
fax: +49 511 762-5333
office location: room 240

Reemt Hinrichs studied Mechatronics at Leibniz Universität Hannover. In May 2017, he finished his Master's Degree with his thesis on "System-theoretical modeling of a structural sound signal path" at the Institut für Informationsverarbeitung (TNT). Since January 2018, he is working as a research assistant towards his PhD degree at the same Institute.

Currently I am working on deep learning models for the compression of electrode excitation patterns of cochlear implants.

Research Interests

  • Signal Coding
  • Cochlear Implants
  • Digital Signal Processing
  • Nonlinear System Theory


Show selected publications only
  • Alexander Lange, Reemt Hinrichs, Jörn Ostermann
    Localized Damage Detection in Wind Turbine Rotor Blades using Airborne Acoustic Emissions (accepted)
    9th Asia-Pacific Workshops on Structural Health Monitoring 2022 (APWSHM 2022), December 2022
  • Reemt Hinrichs, Kai Liang, Ze Lu, Jörn Ostermann
    Improved Compression of Artificial Neural Networks through Curvature-Aware Training
    Proceedings of the IEEE World Congress on Computational Intelligence, July 2022
  • Alexander Lange, Max Käding, Reemt Hinrichs, Jörn Ostermann, Steffen Marx
    Wire Break Detection in Bridge Tendons Using Low-Frequency Acoustic Emissions
    European Workshop on Structural Health Monitoring. EWSHM 2022., Springer, June 2022
  • Reemt Hinrichs, Nan Jiang, Raul Beltran, Thomas Krause, Max Käding, Alexander Lange, Boso Schmidt, Jörn Ostermann, Steffen Marx
    Analysis of the Repeatability of the Pencil Lead Break in Comparison to the Ball Impact and Electromagnetic Body-Noise Actuator
    20th World Conference on Non-Destructive Testing (WCNDT 2020), 2022
  • Reemt Hinrichs, Kevin Gerkens, Alexander Lange, Jörn Ostermann
    Classification of Guitar Effects and Extraction of their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks
    EvoMUSART 2022, 2022
  • Reemt Hinrichs, Kevin Gerkens, Jörn Ostermann
    Convolutional Neural Networks for the Classification of Guitar Effects and Extraction of the Parameter Settings of Single and Multi Guitar Effects from Instrument Mixes
    EURASIP Journal on Audio, Speech, and Music Processing, 2022
  • Reemt Hinrichs, Hendrik Heise, Lukas: Ostermann Ehmann
    Lossless Compression at Zero Delay of the Electrical Stimulation Patterns of Cochlear Implants for Wireless Streaming of Audio Using Artificial Neural Networks
    7th International Conference on Frontiers of Signal Processing, 2022
  • Reemt Hinrichs, Felix Ortmann, Jörn Ostermann
    Vector-Quantized Zero-Delay Deep Autoencoders for the Compression of Electrical Stimulation Patterns of Cochlear Implants Using STOI
    IEEE EMBS 2022, 2022
  • Reemt Hinrichs, Jonas Dunkel, Jörn Ostermann
    Mixing Time-Frequency Distributions for Speech Command Recognition using Convolutional Neural Networks
    6th International Conference on Frontiers of Signal Processing (ICFSP 2021), September 2021
  • Reemt Hinrichs, Tom Gajecki, Jörn Ostermann, Waldo Nogueira
    A subjective and objective evaluation of a codec for the electrical stimulation patterns of cochlear implants
    Journal of the Acoustic Society of America, March 2021
  • Reemt Hinrichs, Alexander Schmidt, Julian Koslowski, Jörn Ostermann, Berend Denkena
    Analysis of the impact of data compression on condition monitoring algorithms for ball screws
    CMMO CIRP 2021, 2021
  • Reemt Hinrichs, Nan Jiang, Raul Beltran, Thomas Krause, Alexander Lange, Max Käding, Boso Schmidt, Steffen Marx, Jörn Ostermann,
    Analysis of the repeatability of the pencil lead break artificial sound source
    59th Annual British Conference on Non-Destructive Testing, September 2020
  • Jörn Ostermann, Reemt Hinrichs
    Links und rechts verbinden
    Unimagazin, Leibniz Universität Hannover, No. 1, June 2020
  • Henrik Jürgens, Reemt Hinrichs, Jörn Ostermann
    Recognizing Guitar Effects and Their Parameter Settings
    Proceedings of the DAFx2020 (Vol I), 2020
  • Jörn Ostermann Reemt Hinrichs
    Signal Coding for Binaural Signal Processing in Cochlear Implants
    Binaire, October 2019
  • Reemt Hinrichs, Tom Gajecki, Jörn Ostermann, Waldo Nogueira
    Coding of Electrical Stimulation Patterns for Binaural Sound Coding Strategies for Cochlear Implants
    41st International Engineering in Medicine and Biology Conference, July 2019
  • Reemt Hinrichs, Thomas Krause, Max Käding, Jörn Ostermann, Steffen Marx
    Measurement-Based Model of Structural Sound Transmission in a Concrete Specimen
    12th European Conference on Non-Destructive Testing (12th ECNDT), June 2018
Other activities

Supervised theses

Cochlear Implants:

  • Nonlinear Prediction of Electrode Excitation Patterns in Cochlear Implants using Artificial Neural Networks
  • Verlustlose Codierung von Erregungsmustern für Cochlea-Implantate
  • Estimation of Background Noise based on Excitation Patterns in Cochlear Implants
  • Kompression von Elektrodenerregungsmustern in Cochlea-Implantaten mittels Autoencodern
  • Audiocodierung mittels WaveNet im Kontext von Cochlea-Implantaten
  • Investigation of Algorithms for Phase Reconstruction from Spectrograms

System Modelling:

  • Geometry Dependent Modeling of the Transfer Function for the Structure-borne Sound in Concrete Girders
  • Comparison of Different Signal Sources for the Determination of the Transfer Function of Structure-Borne Noise in Concrete
  • Empirical Determination of the Transfer Function of Structure-Borne Sound Using Finite-Element-Analysis and Genetic Programming
  • Parametric study of the impulse response of concrete beams using finite element analysis


  • Estimation of the Upper Bound of the Prediction Gain of Linear and Nonlinear Predictors using the Automutual Information Function
  • Automatische Extraktion von Gitarreneffekten
  • Verlustbehaftete Codierung von Maschinendaten im Kontext der Zustands- und Prozessüberwachung
  • Entwicklung und Bewertung von verlustbehafteten Datenkompression von Werkzeugmaschinendaten zur Prozess- und Zustandsüberwachung
  • Analyse der Wigner-Verteilungsfunktion im Vergleich zur Kurzzeit-Fouriertransformation
  • Vergleich von Zeit-Frequenzverteilungen für die Audioklassifikation mittels Künstlicher Neuronaler Netze
  • Metalernen von Aktivierungsfunktionen für neuronale Netze mittels orthogonaler Basisfunktionen
  • Metalernen von Aktivierungsfunktion von neuronalen Netzen mittels evolutionärer Algorithmen