  Unsupervised anomalous sound detection (ASD) aims to identify anomalous
sounds by learning the features of normal operational sounds and sensing their
deviations. Recent approaches have focused on the self-supervised task
utilizing the classification of normal data, and advanced models have shown
that securing representation space for anomalous data is important through
representation learning yielding compact intra-class and well-separated
intra-class distributions. However, we show that conventional approaches often
fail to ensure sufficient intra-class compactness and exhibit angular disparity
between samples and their corresponding centers. In this paper, we propose a
training technique aimed at ensuring intra-class compactness and increasing the
angle gap between normal and abnormal samples. Furthermore, we present an
architecture that extracts features for important temporal regions, enabling
the model to learn which time frames should be emphasized or suppressed.
Experimental results demonstrate that the proposed method achieves the best
performance giving 0.90%, 0.83%, and 2.16% improvement in terms of AUC, pAUC,
and mAUC, respectively, compared to the state-of-the-art method on DCASE 2020
Challenge Task2 dataset.
