Design Choices for Learning Embeddings from Auxiliary Tasks for Domain Generalization in Anomalous Sound Detection

Emitted machine sounds can change drastically due to a change in settings of machines or varying noise conditions resulting in false alarms when monitoring machine conditions with a trained anomalous sound detection (ASD) system. In this work, a conceptually simple state-of-the-art ASD system based on embeddings learned through auxiliary tasks generalizing to multiple data domains is presented. In experiments conducted on the DCASE 2022 ASD dataset, particular design choices such as preventing trivial projections, combining multiple input representations and choosing a suitable back-end are shown to significantly improve the ASD performance.