论文标题
事件驱动的光谱特征特征提取和使用硅耳蜗模型分类
Event-driven Spectrotemporal Feature Extraction and Classification using a Silicon Cochlea Model
论文作者
论文摘要
本文介绍了在现场可编程栅极阵列（FPGA）上基于事件的双耳耳蜗系统的可重构数字实现。它由一对具有快速作用压缩（CAR FAC）耳蜗模型的不对称谐振器以及泄漏的整合和火（LIF）神经元组成。此外，我们提出了使用自适应选择阈值（盛宴）提取事件驱动的光谱频段接受场（STRF）特征。它在Tidigtis基准测试中进行了测试，并将其与当前基于事件的听觉信号处理方法和神经网络进行了比较。
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.
