  Unsupervised anomalous sound detection is concerned with identifying sounds
that deviate from what is defined as 'normal', without explicitly specifying
the types of anomalies. A significant obstacle is the diversity and rareness of
outliers, which typically prevent us from collecting a representative set of
anomalous sounds. As a consequence, most anomaly detection methods use
unsupervised rather than supervised machine learning methods. Nevertheless, we
will show that anomalous sound detection can be effectively framed as a
supervised classification problem if the set of anomalous samples is carefully
substituted with what we call proxy outliers. Candidates for proxy outliers are
available in abundance as they potentially include all recordings that are
neither normal nor abnormal sounds. We experiment with the machine condition
monitoring data set of the 2020's DCASE Challenge and find proxy outliers with
matching recording conditions and high similarity to the target sounds
particularly informative. If no data with similar sounds and matching recording
conditions is available, data sets with a larger diversity in these two
dimensions are preferable. Our models based on supervised training with proxy
outliers achieved rank three in Task 2 of the DCASE2020 Challenge.
