First-Shot Unsupervised Anomalous Sound Detection With Unknown Anomalies Estimated by Metadata-Assisted Audio Generation

• Published in: arXiv.org
• Main Authors: Zhang, Hejing, Zhu, Qiaoxi, Guan, Jian, Liu, Haohe, Xiao, Feiyang, Tian, Jiantong, Mei, Xinhao, Liu, Xubo, Wang, Wenwu
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 11.03.2024
• Subjects: Acoustics; Anomalies; Audio data; Availability; Metadata; Probabilistic models; Sound

First-shot (FS) unsupervised anomalous sound detection (ASD) is a brand-new task introduced in DCASE 2023 Challenge Task 2, where the anomalous sounds for the target machine types are unseen in training. Existing methods often rely on the availability of normal and abnormal sound data from the targe...