Event-driven Spectrotemporal Feature Extraction and Classification using a Silicon Cochlea Model
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Dec 2022.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2023A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observationscited by 2
- 2026Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGAcited by 2
- 2023Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGAcited by 1
- 2025Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGAcited by 1
- 2026Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA18 Feb 2026
- 2025Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA9 Mar 2025
- 2023A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations6 Jul 2023
- 2023Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA31 May 2023