Abstract:

Emitted sounds may drastically change when using different microphones, when properties of the sound sources change, or when recording in different acoustic environments. Ideally, anomalous sound detection (ASD) systems should be able to generalize well to unseen target domains by only providing a few target domain samples to define how normal data samples sound like, without needing to re-train or modify the system. In contrast with the source domain, for which many normal training samples are available, accurately estimating the underlying distribution of normal data after a domain shift based on very few samples is challenging. This usually leads to a mismatch between the corresponding anomaly scores of source and target domains and significantly reduces performance. In this work, we propose a framework for re-scaling anomaly scores based on the ratio between the cosine distance of a test sample to a normal reference sample and the distances to this sample’s next-closest neighbors in the reference set. In experimental evaluations, it is shown that the re-scaled anomaly scores reduce the domain mismatch for multiple domains. As a result, we obtain new state-of-the-art performances on the DCASE2020 and DCASE2023 ASD datasets