Description and Discussion on DCASE 2025 Challenge Task 2: First-shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918148565893120 |
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| author | Nishida, Tomoya Harada, Noboru Niizumi, Daisuke Albertini, Davide Sannino, Roberto Pradolini, Simone Augusti, Filippo Imoto, Keisuke Dohi, Kota Purohit, Harsh Endo, Takashi Kawaguchi, Yohei |
| author_facet | Nishida, Tomoya Harada, Noboru Niizumi, Daisuke Albertini, Davide Sannino, Roberto Pradolini, Simone Augusti, Filippo Imoto, Keisuke Dohi, Kota Purohit, Harsh Endo, Takashi Kawaguchi, Yohei |
| contents | This paper introduces the task description for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge Task 2, titled "First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring". Building on the DCASE 2024 Challenge Task 2, this task is structured as a first-shot problem within a domain generalization framework. The primary objective of the first-shot approach is to facilitate the rapid deployment of ASD systems for new machine types without requiring machine-specific hyperparameter tunings. For DCASE 2025 Challenge Task 2, sounds from previously unseen machine types have been collected and provided as the evaluation dataset. We received 119 submissions from 35 teams, and an analysis of these submissions has been made in this paper. Analysis showed that various approaches can all be competitive, such as fine-tuning pre-trained models, using frozen pre-trained models, and training small models from scratch, when combined with appropriate cost functions, anomaly score normalization, and use of clean machine and noise sounds. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_10097 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Description and Discussion on DCASE 2025 Challenge Task 2: First-shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Nishida, Tomoya Harada, Noboru Niizumi, Daisuke Albertini, Davide Sannino, Roberto Pradolini, Simone Augusti, Filippo Imoto, Keisuke Dohi, Kota Purohit, Harsh Endo, Takashi Kawaguchi, Yohei Sound Audio and Speech Processing This paper introduces the task description for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge Task 2, titled "First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring". Building on the DCASE 2024 Challenge Task 2, this task is structured as a first-shot problem within a domain generalization framework. The primary objective of the first-shot approach is to facilitate the rapid deployment of ASD systems for new machine types without requiring machine-specific hyperparameter tunings. For DCASE 2025 Challenge Task 2, sounds from previously unseen machine types have been collected and provided as the evaluation dataset. We received 119 submissions from 35 teams, and an analysis of these submissions has been made in this paper. Analysis showed that various approaches can all be competitive, such as fine-tuning pre-trained models, using frozen pre-trained models, and training small models from scratch, when combined with appropriate cost functions, anomaly score normalization, and use of clean machine and noise sounds. |
| title | Description and Discussion on DCASE 2025 Challenge Task 2: First-shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.10097 |