Annotation-free Fine-tuning for Unsupervised Anomalous Sound Detection

The goal of unsupervised anomalous sound detection (ASD) for industrial machines is to identify anomalous sounds using only normal sounds for training. A common method is to use attribute information as auxiliary labels to train ASD models. However, this approach often faces the challenge of obtaining auxiliary attribute information in complex industrial environments. This paper proposes a novel unsupervised anomalous sound detection method that fine-tunes pre-trained models without using attribute information, achieved by training a machine type classifier. The proposed method is called Annotation-Free Fine-tuning (AFF) for unsupervised anomalous sound detection. In addition, we propose an anomaly score calculation method that combines the machine type classifier with an unsupervised anomalous sound estimator, further improving the anomalous detection performance of AFF. Experiments on DCASE 2024 Task 2 development dataset indicate that our method outperforms other typical ASD methods that do not utilize attribute information.