Distributed collaborative anomalous sound detection by embedding sharing
Kota Dohi Yohei Kawaguchi
To develop a machine sound monitoring system, a method for detecting
anomalous sound is proposed. In this paper, we explore a method for multiple
clients to collaboratively learn an anomalous sound detection model while
keeping their raw data private from each other. In the context of industrial
machine anomalous sound detection, each client possesses data from different
machines or different operational states, making it challenging to learn
through federated learning or split learning. In our proposed method, each
client calculates embeddings using a common pre-trained model developed for
sound data classification, and these calculated embeddings are aggregated on
the server to perform anomalous sound detection through outlier exposure.
Experiments showed that our proposed method improves the AUC of anomalous sound
detection by an average of 6.8%.