Anomaly detection for environmental noise monitoring
Octave-band sound pressure level is the preferred measure for continuous environmental noise monitoring over raw audio because accepted standards and devices exist, these data do not compromise voice privacy, and thus an octave-band sound meter can legally collect data in public. By setting up an experiment that continuously monitors octave-band sound pressure level in a residential street, we show daily noise-level patterns correlated to human activities. Directly applying well-known anomaly detection algorithms including one-class support vector machine, replicator neural network, and principal component analysis based anomaly detection shows low performance in the collected data because these standard algorithms are unable to exploit the daily patterns. Therefore, principal component analysis anomaly detection with time-varying mean and the covariance matrix over each hour, is proposed in order to detect abnormal acoustic events in the octave band measurements of the residential-noise-monitoring application. The proposed method performs at 0.83 in recall, 0.88 in precision and 0.85 in F-measure on the evaluation data set.
Anomaly detection, environmental noise monitoring
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign