Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring
Persistent acoustic monitoring can detect machine faults without physical contact, but always-on inference is constrained by power, latency, and deployment complexity. We demonstrate autoencoder-based acoustic anomaly detection on an Intel Loihi 2 neuromorphic processor under clean and noisy conditions. Log-mel features are computed off chip; normalization, autoencoder inference, L1 reconstruction scoring, and thresholding run on chip. In a clean, microphone-position-invariant ToyADMOS ToyCar benchmark, the on-chip model achieves 0.9959 AUC and 0.9785 standardized pAUC at maximum false-positive rate 0.1. In the DCASE 2026 Task 2 ToyCar noisy benchmark, the model achieves source AUC 0.7990...
| Authors | Steven C. Nesbit, Victor M. Vergara, Michael A. Felix et al. |
|---|---|
| Primary category | cs.NE |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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