  The state-of-the-art approach for semi-supervised anomalous sound detection
is to first learn an embedding space by using auxiliary classification tasks
based on meta information or self-supervised learning and then estimate the
distribution of normal data. In this work, AdaProj a novel loss function for
training the embedding model is presented. In contrast to commonly used angular
margin losses, which project data of each class as close as possible to their
corresponding class centers, AdaProj learns to project data onto class-specific
subspaces while still ensuring an angular margin between classes. By doing so,
the resulting distributions of the embeddings belonging to normal data are not
required to be as restrictive as other loss functions allowing a more detailed
view on the data. In experiments conducted on the DCASE2022 and DCASE2023
anomalous sound detection datasets, it is shown that using AdaProj to learn an
embedding space significantly outperforms other commonly used loss functions.
