Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model.

• DOI: 10.48550/arxiv.2212.07136
• Publication Date: 2023-04-18
• Authors: Xu, Ying (R19792); Perera, Samalika ...; Bethi, Yeshwanth ...; Afshar, Saeed (R1...; Schaik, Andre van...
• Keywords: Signal Processing (eess.SP); FOS: Computer and information sciences; Sound (cs.SD); LIF; Neurosciences. Biological psychiatry. Neuropsychiatry; 02 engineering and technology; event-based feature extraction; Computer Science - Sound; CAR-FAC; FEAST; electronic cochlea; Audio and Speech Processing (eess.AS); XXXXXX - Unknown; 0202 electrical engineering, electronic engineering, information engineering; FOS: Electrical engineering, electronic engineering, information engineering; Electrical Engineering and Systems Science - Signal Processing; neuromorphic engineering; RC321-571; Electrical Engineering and Systems Science - Audio and Speech Processing; Neuroscience

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.