arXiv AI
Jun 17
A Neuromorphic Trigger for Efficient Audio Event Detection
arXiv:2606. 17775v1 Announce Type: cross Abstract: Efficient processing of continuous audio streams remains a key challenge for real-time and resource-constrained systems.
Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring
The paper presents an autoencoder-based acoustic anomaly detection system implemented on Intel’s Loihi 2 neuromorphic processor. It achieves high detection performance—0.9959 AUC on a clean ToyADMOS ToyCar benchmark and 0.7990 source AUC on a noisy DCASE 2026 Task 2 ToyCar benchmark—while operating with only 0.0406–0.0426 mJ of dynamic energy per sample, far below CPU and GPU baselines. The system demonstrates that low‑power, on‑chip inference is feasible for persistent machine monitoring.
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