Abstract:
The goal of anomalous sound detection is to unsupervisedly train a system to distinguish normal from anomalous sounds that substantially differ from the normal sounds used for training. In this paper, a system based on Look, Listen, and Learn embeddings, which participated in task 2 “Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring” of the DCASE challenge 2020 and is adapted from an open-set machine listening system, is presented. The experimental results show that the presented system significantly outperforms the baseline system of the challenge both in detecting outliers and in recognizing the correct machine type or exact machine id. Moreover, it is shown that an ensemble consisting of the presented system and the baseline system performs even better than both of its components.
