Reemt Hinrichs

Reemt Hinrichs is a researcher at the Institute for Information Processing (TNT) at Leibniz University Hannover, where he focuses on signal processing applications across biomedical engineering, audio technology, and structural health monitoring. He completed his Dr.-Ing. (PhD) at TNT in 2023 after working as a research assistant since January 2018.

Dr. Hinrichs earned his Master's Degree in Mechatronics from Leibniz University Hannover in May 2017, completing his thesis on "System-theoretical modeling of a structural sound signal path" at the Institute for Information Processing. His academic journey reflects a consistent focus on signal processing theory applied to practical engineering challenges.

His primary research interests center around Signal Coding, particularly for Cochlear Implants, along with broader expertise in Digital Signal Processing and Nonlinear System Theory. His work spans multiple application domains including biomedical engineering (cochlear implants), audio processing (guitar effects modeling), and structural health monitoring (acoustic emissions analysis for infrastructure).

Dr. Hinrichs' publication record demonstrates a strong focus on compression algorithms for cochlear implants, with numerous papers on neural network-based approaches for zero-delay compression of electrical stimulation patterns. He has also made significant contributions to guitar effects modeling using convolutional neural networks and structural health monitoring through acoustic emission analysis. His research bridges theoretical signal processing with practical applications across diverse domains, showing particular strength in applying deep learning techniques to specialized signal processing challenges.

With approximately 60 theses supervised, Dr. Hinrichs has been actively involved in mentoring students across various research topics including cochlear implant technology, structural modeling, and audio signal processing. His supervision portfolio includes work on nonlinear prediction of electrode excitation patterns, geometry-dependent modeling of transfer functions, and automatic extraction of guitar effects.

His current research focuses on "deep learning models for the compression of electrode excitation patterns of cochlear implants," continuing his long-standing expertise in this specialized area of biomedical signal processing while expanding into new applications of neural network architectures for real-time signal compression.