  Anomalous sound detection systems must detect unknown, atypical sounds using
only normal audio data. Conventional methods use the serial method, a
combination of outlier exposure (OE), which classifies normal and
pseudo-anomalous data and obtains embedding, and inlier modeling (IM), which
models the probability distribution of the embedding. Although the serial
method shows high performance due to the powerful feature extraction of OE and
the robustness of IM, OE still has a problem that doesn't work well when the
normal and pseudo-anomalous data are too similar or too different. To
explicitly distinguish these data, the proposed method uses multi-task learning
of two binary cross-entropies when training OE. The first is a loss that
classifies the sound of the target machine to which product it is emitted from,
which deals with the case where the normal data and the pseudo-anomalous data
are too similar. The second is a loss that identifies whether the sound is
emitted from the target machine or not, which deals with the case where the
normal data and the pseudo-anomalous data are too different. We perform our
experiments with DCASE 2021 Task~2 dataset. Our proposed single-model method
outperforms the top-ranked method, which combines multiple models, by 2.1% in
AUC.
