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

arXiv: https://arxiv.org/abs/2212.07136 · HTML: https://mlsanity.com/paper/2212.07136/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2022.

## Built on by (most cited first, 4 of 4)

- [A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations](https://mlsanity.com/paper/2307.02947/) (2023)
- [Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA](https://mlsanity.com/paper/2602.16442/) (2026)
- [Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA](https://mlsanity.com/paper/2305.19468/) (2023)
- [Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA](https://mlsanity.com/paper/2503.06629/) (2025)

## Abstract (arXiv)

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.