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arXiv · 2212.07136

Event-driven Spectrotemporal Feature Extraction and Classification using a Silicon Cochlea Model

Ying Xu·Samalika Perera·Yeshwanth Bethi·Saeed Afshar·André van Schaik
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Abstract

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.

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Ying Xu, Samalika Perera, Yeshwanth Bethi, Saeed Afshar, André van Schaik. 2022-12-14. Event-driven Spectrotemporal Feature Extraction and Classification using a Silicon Cochlea Model. https://arxiv.org/abs/2212.07136

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