{"ID":2869786,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13853","arxiv_id":"2509.13853","title":"Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detection","abstract":"Unsupervised anomalous sound detection aims to detect unknown anomalous sounds by training a model using only normal audio data. Despite advancements in self-supervised methods, the issue of frequent false alarms when handling samples of the same type from different machines remains unresolved. This paper introduces a novel training technique called one-stage supervised contrastive learning (OS-SCL), which significantly addresses this problem by perturbing features in the embedding space and employing a one-stage noisy supervised contrastive learning approach. On the DCASE 2020 Challenge Task 2, it achieved 94.64\\% AUC, 88.42\\% pAUC, and 89.24\\% mAUC using only Log-Mel features. Additionally, a time-frequency feature named TFgram is proposed, which is extracted from raw audio. This feature effectively captures critical information for anomalous sound detection, ultimately achieving 95.71\\% AUC, 90.23\\% pAUC, and 91.23\\% mAUC. The source code is available at: \\underline{www.github.com/huangswt/OS-SCL}.","short_abstract":"Unsupervised anomalous sound detection aims to detect unknown anomalous sounds by training a model using only normal audio data. Despite advancements in self-supervised methods, the issue of frequent false alarms when handling samples of the same type from different machines remains unresolved. This paper introduces a...","url_abs":"https://arxiv.org/abs/2509.13853","url_pdf":"https://arxiv.org/pdf/2509.13853v2","authors":"[\"Shun Huang\",\"Zhihua Fang\",\"Liang He\"]","published":"2025-09-17T09:38:47Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.CL\"]","methods":"[]","has_code":false}