Auditory Anomaly Detection using Recurrent Spiking Neural Networks

Brain-inspired networks promise capabilities of achieving high computational efficacy with low energy footprint. Auditory perception systems are resource constrained when deployed on low power edge AI devices. Hence, we employ spiking neural networks (SNNs) for auditory scene analysis, specifically targeting temporal detection of anomaly cues particularly siren sounds. We generate artificial audio sequences from a publicly available dataset containing various siren and noise sounds. We train small-scale recurrent SNNs with leaky-integrate-and-fire (LIF) neurons in the hidden layer and achieve accurate predictions with precious few parameters. Further, we provide a baseline for conventional RNNs of similar network topology on the same task. With comparable accuracy, reduced parameter, and sparse spiking activity in hidden layer in contrast to conventional methods, we found bio-inspired approach realized using SNNs to be promising in solving the time-series auditory anomaly detection task.