  In this paper, we introduce ID-Conditioned Auto-Encoder for unsupervised
anomaly detection. Our method is an adaptation of the Class-Conditioned
Auto-Encoder (C2AE) designed for the open-set recognition. Assuming that
non-anomalous samples constitute of distinct IDs, we apply Conditioned
Auto-Encoder with labels provided by these IDs. Opposed to C2AE, our approach
omits the classification subtask and reduces the learning process to the single
run. We simplify the learning process further by fixing a constant vector as
the target for non-matching labels. We apply our method in the context of
sounds for machine condition monitoring. We evaluate our method on the ToyADMOS
and MIMII datasets from the DCASE 2020 Challenge Task 2. We conduct an ablation
study to indicate which steps of our method influences results the most.
