Few-Shot Anomalous Sound Detection Based on Anomaly Map Estimation Using Pseudo Abnormal Data

This paper proposes a novel anomalous sound detection based on anomaly map estimation. Different from conventional autoencoder-based schemes, given a log-mel spectrogram image, our proposed method predicts an anomaly score map that indicates an anomalous part in the image. We generate pseudo abnormal data from normal data by CutPaste, then exploit them for model training. In real-world environments, the small number of real anomalies may be often available. Therefore, we also employ a few-shot learning architecture, so that not only normal data but also anomalies can be used for model training. Experiments were conducted to clarify the effectiveness of our methods, using a common machine-sound dataset. It is shown that the proposed methods outperform baseline methods. We also compared our method with few-shot learning to the state-of-the-art method. It is finally found that the proposed scheme has significant effectiveness in anomalous sound detection.