An Ensemble Approach to Anomalous Sound Detection Based on Conformer-Based Autoencoder and Binary Classifier Incorporated with Metric Learning

This paper presents an ensemble approach based on two unsupervised anomalous sound detection (ASD) methods for machine condition monitoring under domain-shifted conditions in DCASE 2021 challenge Task 2. The first ASD method is based on a conformerbased sequence-level autoencoder with section ID regression and a self-attention architecture. We utilize the data augmentation techniques such as SpecAugment to boost the performance and combine a simple scorer module for each section and each domain to address the domain shift problem. The second ASD method is based on a binary classification model using metric learning that uses task-irrelevant outliers as pseudo-anomalous data while controlling centroids of normal and outlier data in a feature space. As a countermeasure against the domain shift problem, we perform data augmentation based on Mixup with data from the target domain, resulting in a stable performance for each section. An ensemble approach is applied to each method, and the resulting two ensembled methods are further ensembled to maximize the ASD performance. The results of DCASE 2021 challenge Task 2 have demonstrated that our proposed method achieves a harmonic mean of 63.745% of area under the curve (AUC) and partial AUC (p = 0.1) over all machines, sections, and domains.