ANOMALY MACHINE DETECTION ALGORITHM BASED ON SEMI VARIATIONAL AUTO-ENCODER OF MEL SPECTROGRAM Technical Report

This report proposes a solution for Task 2 of IEEE DCASE data challenge 2020, which attempts to detect anomaly machines according to acoustic data. The proposed solution uses a semi variational auto-encoder. The term “semi” indicates that the resulting variational auto-encoder may not successfully reconstruct the input as the key task of the task is to distinguish the outlier samples according to a specific feature rather than reconstruct the input precisely. As a result, there are a few minor changes introduced by the provided baseline system, which set up a different training stop criteria and a different anomaly scoring system. By the proposed method, the use of different stop training criteria for an variational auto-encoder may help different objectives.