Abstract
Deciding whether a sound is anomalous is accomplished by comparing it to a learnt distribution of inliers. Therefore, learning a distribution close to the true population of inliers is vital for anomalous sound detection (ASD). Data engineering is a common strategy to aid training and improve generalisation. However, in the context of ASD, it is debatable whether data engineering indeed facilitates generalisation or whether it obscures characteristics that distinguish anomalies from inliers. We conduct an exploratory investigation into this by focusing on frequency-related data engineering. We adapt local model explanations to anomaly detectors and show that models rely on higher frequencies to distinguish anomalies from inliers. We verify this by filtering the input data's frequencies and observing the change in ASD performance. Our results indicate that sifting out low frequencies by applying high-pass filters aids downstream performance, and this could serve as a simple pre-processing step for improving anomaly detectors.