A Neuromorphic Trigger for Efficient Audio Event Detection
Efficient processing of continuous audio streams remains a key challenge for real-time and resource-constrained systems. This paper introduces a neuromorphic trigger for audio event detection, based on a spiking neural network (SNN) that selectively gates input to downstream models. The proposed neuromorphic trigger acts as a flexible low-cost front-end, identifying salient audio segments and enabling these to be processed by a more computationally intensive model for tasks such as classification. The trigger is implemented as a lightweight fully connected SNN using a close-open filter for postprocessing, and is evaluated on two representative tasks: Anomalous Sound Detection (ASD) and So...
| Authors | Benjamin Hatton, Oliver Rhodes, Luca Peres |
|---|---|
| Primary category | cs.SD |
| Published | 16 June 2026 |
| Last updated at source | 24 June 2026 |
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