@inproceedings{55cde67838f74c9696de28642b268070,
title = "HIERARCHICAL METADATA INFORMATION CONSTRAINED SELF-SUPERVISED LEARNING FOR ANOMALOUS SOUND DETECTION UNDER DOMAIN SHIFT",
abstract = "Self-supervised learning methods have achieved promising performance for anomalous sound detection (ASD) under domain shift by incorporating the metadata of domain shift types and machine sound attributes in feature learning. However, the relation between domain shifts and machine sound attributes has yet to be fully utilised despite their potential benefits for characterising domain shifts. This paper presents a hierarchical metadata information constrained self-supervised ASD method, where the hierarchical relation between domain shift types (section IDs) and attributes is constructed and used as constraints to improve feature representation. In addition, we propose an attribute-group-centre based method for calculating the anomaly score under the domain shift condition. Experiments show improved audio feature learning over the state-of-the-art methods in DCASE 2022 challenge Task 2.",
keywords = "Anomalous sound detection, domain shift, metadata, self-supervised learning",
author = "Haiyan Lan and Qiaoxi Zhu and Jian Guan and Yuming Wei and Wenwu Wang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
year = "2024",
doi = "10.1109/ICASSP48485.2024.10446044",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7670--7674",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings",
address = "美国",
}