Detection of anomaly acoustic scenes based on a temporal dissimilarity model

This paper proposes detection of anomaly acoustic scenes based on a temporal dissimilarity model. The periodicity in the temporal variation of acoustic scenes is first pointed out and then used to build a new stochastic model. In the new model, the temporal variation is expressed by dissimilarity between current and previous acoustic scenes. Anomaly acoustic scenes are detected based on the 24-hour periodic dissimilarity model. Evaluation results using 40-day (1000-hour) data show that the proposed method can detect unknown anomaly acoustic scenes with 82.3% F-measure in 0 dB signal-to-noise-ratio conditions.