Anomaly Detection from a Frequency Perspective: M-Band Wavelet Packet Anomaly Detection Network

Abstract

Anomaly detection is a task of identifying samples that significantly differ from the majority. However, most typical anomaly detection methods often prioritize accuracy over interpretability. To address this limitation, we propose an explainable anomaly detection approach using a deep learnable M-band wavelet packet network constructed from the frequency perspective. This network could flexibly decompose a signal into different frequency bands and learn its frequency representation. Then the learnable threshold function is designed to learn the distribution of the normal signal in each frequency band and corrupt the abnormal representation. As a result, the abnormal signal can not be reconstructed from its corrupted representation. We evaluate the proposed method on both simulation data and real acoustic data.