When training a model for anomalous sound detection, one usually needs to estimate the underlying distribution of the normal data. By doing so, anomalous data has a lower probability in view of this distribution than normal data and thus can easily be detected. However, audio data is very high-dimensional making it difficult to have a good estimate of the true distribution. To have more accurate estimates, the dimension of the data can be reduced first. One way to do this is to train discriminative neural networks for extracting lower-dimensional representations of the data. Particularly, neural networks trained with angular margin losses as AdaCos have been shown to perform well for this task. In this work, a modified AdaCos loss called sub-cluster AdaCos specifically designed for detecting anomalous data is presented. In multiple experiments conducted on the DCASE 2020 dataset for "Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring", these design choices are empirically justified. As a result, a conceptually simple system for anomalous sound detection is presented that significantly outperforms all other published systems on this dataset.
