Demo Abstract: Acoustic Anomaly Detection System

Abstract Acoustic signals contain rich information of the environment. They can be used for detecting anomalous events such as in automated machine monitoring. In this demonstration, we present our acoustic anomaly detection system that captures acoustic signals and classifies them using machine learning techniques. Our system includes a server for sound management and model training, a mobile client for sound capturing and real-time classification, and a workbench that acts as a user interface. We will show the full operational pipeline of our system in this demonstration.
  • Jae-wook Ahn (IBM US)
  • Keith Grueneberg (IBM US)
  • Bongjun Ko (IBM US)
  • Wei-Han Lee
  • Eduardo Morales (IBM US)
  • Shiqiang Wang (IBM US)
  • Xiping Wang (IBM US)
  • David Wood (IBM US)
Date Nov-2019
Venue SenSys 2019: Proceedings of the 17th Conference on Embedded Networked Sensor Systems [link]