  Unsupervised anomalous sound detection aims to detect unknown abnormal sounds
of machines from normal sounds. However, the state-of-the-art approaches are
not always stable and perform dramatically differently even for machines of the
same type, making it impractical for general applications. This paper proposes
a spectral-temporal fusion based self-supervised method to model the feature of
the normal sound, which improves the stability and performance consistency in
detection of anomalous sounds from individual machines, even of the same type.
Experiments on the DCASE 2020 Challenge Task 2 dataset show that the proposed
method achieved 81.39\%, 83.48\%, 98.22\% and 98.83\% in terms of the minimum
AUC (worst-case detection performance amongst individuals) in four types of
real machines (fan, pump, slider and valve), respectively, giving 31.79\%,
17.78\%, 10.42\% and 21.13\% improvement compared to the state-of-the-art
method, i.e., Glow\_Aff. Moreover, the proposed method has improved AUC
(average performance of individuals) for all the types of machines in the
dataset. The source codes are available at
https://github.com/liuyoude/STgram_MFN
