Neuromorphic acoustic sensing using an adaptive microelectromechanical cochlea with integrated feedback
Claudia Lenk, Philipp Hövel, Kalpan Ved, Steve Durstewitz, Thomas Meurer, Tobias Fritsch, Andreas Männchen, Jan Küller, Daniel Beer, Tzvetan Ivanov, Martin Ziegler
Abstract
Abstract 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.