How can we detect anomalies from subsampled audio signals?

Abstract

We aim to reduce the cost of sound monitoring for maintain machinery by reducing the sampling rate, i.e., sub-Nyquist sampling. Monitoring based on sub-Nyquist sampling requires two sub-systems: a sub-system on-site for sampling machinery sounds at a low rate and a sub-system off-site for detecting anomalies from the subsampled signal. This paper proposes a method for achieving both subsystems. First, the proposed method uses non-uniform sampling to encode higher than the Nyquist frequency. Second, the method applies a long short-term memory-(LSTM)-based autoencoder network for detecting anomalies. The novelty of the proposed network is that the subsampled time-domain signal is demultiplexed and received as input in an end-to-end manner, enabling anomaly detection from the subsampled signal. Experimental results indicate that our method is suitable for anomaly detection from the subsampled signal.