Self-Supervised Learning for Anomalous Sound Detection
Kevin Wilkinghoff
State-of-the-art anomalous sound detection (ASD) systems are often trained by
using an auxiliary classification task to learn an embedding space. Doing so
enables the system to learn embeddings that are robust to noise and are
ignoring non-target sound events but requires manually annotated meta
information to be used as class labels. However, the less difficult the
classification task becomes, the less informative are the embeddings and the
worse is the resulting ASD performance. A solution to this problem is to
utilize self-supervised learning (SSL). In this work, feature exchange
(FeatEx), a simple yet effective SSL approach for ASD, is proposed. In
addition, FeatEx is compared to and combined with existing SSL approaches. As
the main result, a new state-of-the-art performance for the DCASE2023 ASD
dataset is obtained that outperforms all other published results on this
dataset by a large margin.