  The goal of Unsupervised Anomaly Detection (UAD) is to detect anomalous
signals under the condition that only non-anomalous (normal) data is available
beforehand. In UAD under Domain-Shift Conditions (UAD-S), data is further
exposed to contextual changes that are usually unknown beforehand. Motivated by
the difficulties encountered in the UAD-S task presented at the 2021 edition of
the Detection and Classification of Acoustic Scenes and Events (DCASE)
challenge, we visually inspect Uniform Manifold Approximations and Projections
(UMAPs) for log-STFT, log-mel and pretrained Look, Listen and Learn (L3)
representations of the DCASE UAD-S dataset. In our exploratory investigation,
we look for two qualities, Separability (SEP) and Discriminative Support
(DSUP), and formulate several hypotheses that could facilitate diagnosis and
developement of further representation and detection approaches. Particularly,
we hypothesize that input length and pretraining may regulate a relevant
tradeoff between SEP and DSUP. Our code as well as the resulting UMAPs and
plots are publicly available.
