This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR FAC) cochlea models and leaky integrate and fire (LIF) neurons. Additionally, we propose an event-driven SpectroTemporal Receptive Field (STRF) Feature Extraction using Adaptive Selection Thresholds (FEAST). It is tested on the TIDIGTIS benchmark and compared with current event-based auditory signal processing approaches and neural networks.
@article{arxiv.2212.07136,
title = {Event-driven Spectrotemporal Feature Extraction and Classification using a Silicon Cochlea Model},
author = {Ying Xu and Samalika Perera and Yeshwanth Bethi and Saeed Afshar and André van Schaik},
journal= {arXiv preprint arXiv:2212.07136},
year = {2022}
}