Amazon takes top three spots in Audio Anomaly Detection Challenge

This week at Amazon Web Services' re:Invent 2020 conference, Amazon announced Amazon Monitron, an end-to-end machine-monitoring system composed of sensors, a gateway, and a machine learning model that detects anomalies in vibration (structure-borne sound) or temperature and predicts when equipment may require maintenance. Machine condition monitoring was also the topic of a challenge at the Workshop on the Detection and Classification of Acoustic Scenes and Events (DCASE 2020), in November, in which Amazon took the top three spots, out of 117 submissions. The challenge was to determine whether the sounds emitted by a machine -- such as a fan, pump, or valve -- were normal or anomalous. Forty academic and industry teams submitted entries, an average of almost three submissions per team. In a pair of papers (paper 1 paper 2) we presented at the workshop, we describe the two different neural-network-based approaches we took in our submissions to the challenge.