FODAVA Partner: Visualizing Audio for Anomaly Detection

Most people who handle money a lot (i.e. cashiers) can identify a lower-quality fake bill instantly just by touching it (wikiHow). Data analysts are like cashiers: a trained data analyst can detect anomalies "at a glance" when data are appropriately transformed. This proposal addresses the type of audio anomalies that human data analysts hear instantly: angry shouting, trucks at midnight on a residential street, gunshots. The human ear detects anomalies of this type rapidly and with high accuracy; for example, rifle magazine insertion clicks are detected with 100% accuracy at 0 dB SNR in white noise, babble, or jungle noise. Unfortunately, a data analyst can listen to only one sound at a time. Visualization shows the analyst many sounds at once, possibly allowing him or her to detect an anomaly several orders of magnitude faster than "real time." This project has successfully rendered large audio data sets, comprising thousands of microphones or thousands of minutes, in the form of interactive graphics that reveal important anomalies at a glance.

This project is a collaboration between ECE and ISL, made possible by the Beckman Institute, and by grant 0807329 from the NSF. For more information, consult the menus at the top of this page, or follow any of these links: