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Neuromorphic acoustic sensing using an adaptive microelectromechanical cochlea with integrated feedback
Open accessAbstract
Many speech processing systems struggle in conditions with low signal-to-noise ratios and in changing acoustic environments. Adaptation at the transduction level with integrated signal processing could help to address this; in human hearing, transduction and signal processing are integrated and can be adaptively tuned for noisy conditions. Here we report a microelectromechanical cochlea as a bio-inspired acoustic sensor with integrated signal processing functionality. Real-time feedback is used to tune the sensing and processing properties, and dynamic switching between linear and nonlinear characteristics improves the detection of signals in noisy conditions, increases the sensor dynamic range and enables adaptation to changing acoustic environments. The transition to nonlinear behaviour is attributed to a Hopf bifurcation and we experimentally validate its dependence on sensor and feedback parameters. We also show that output-signal coupling between two coupled sensors can increase the frequency coverage.
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“Neuromorphic systems—modeled after the spiking logic of biological cochleae—may offer substantial improvements for fieldwork. They deliver orders-of-magnitude energy savings while keeping classification accuracy impressively high”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Lenk et al. 2023
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Metadata
- Authors
- Lenk, C., Hövel, P., Ved, K., Durstewitz, S., Meurer, T., Fritsch, T., Männchen, A., Küller, J., Beer, D., Ivanov, T., & Ziegler, M.
- Affiliations
- Technische Universität Ilmenau (DE)
- University College Cork (IE)
- Karlsruhe Institute of Technology (DE)
- Fraunhofer Institute for Digital Media Technology (DE)
- Corresponding author
- Lenk, Claudia
- ORCID
- Venue
- Nature Electronics
- Published
- 2023-05-04
- Volume
- 6
- Issue
- 5
- Pages
- 370-380
- ISSN
- 2520-1131 (ISSN-L)
- OpenAlex
- W4382644231
- Cited by
- 70
- References
- 58
- Citations by year
- 2026: 17 · 2025: 28 · 2024: 18 · 2023: 7
- FWCI
- 5.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Electronics (publishedVersion, cc-by)
- Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion, cc-by)
- Common Library Network (Der Gemeinsame Bibliotheksverbund) (submittedVersion, cc-by)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- KITopen (cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (434434223)
- Carl-Zeiss-Stiftung (MemWerk)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced MEMS and NEMS Technologies
- Life Sciences › Neuroscience › Sensory Systems › Hearing, Cochlea, Tinnitus, Genetics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Mechanical and Optical Resonators
- Keywords
- bio-inspired sensors, feedback control, Hopf bifurcation, low signal-to-noise ratio detection, frequency coverage, speech processing
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