  This paper proposes an approach for anomalous sound detection that
incorporates outlier exposure and inlier modeling within a unified framework by
multitask learning. While outlier exposure-based methods can extract features
efficiently, it is not robust. Inlier modeling is good at generating robust
features, but the features are not very effective. Recently, serial approaches
are proposed to combine these two methods, but it still requires a separate
training step for normal data modeling. To overcome these limitations, we use
multitask learning to train a conformer-based encoder for outlier-aware inlier
modeling. Moreover, our approach provides multi-scale scores for detecting
anomalies. Experimental results on the MIMII and DCASE 2020 task 2 datasets
show that our approach outperforms state-of-the-art single-model systems and
achieves comparable results with top-ranked multi-system ensembles.
