Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques

• URL: http://arxiv.org/abs/2206.05876v1
• Date: Mon, 13 Jun 2022 02:06:15 GMT
• Title: Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques
• Authors: Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto and Yohei Kawaguchi
• Abstract summary: We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Challenge Task 2: "Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques" In DCASE 2021 Challenge Task 2, we organized an ASD task for handling domain shifts. In this task, it was assumed that the occurrences of domain shifts are known. In 2022 Task 2, we focus on domain generalization techniques that detects anomalies regardless of the domain shifts.
• Score: 35.54126113756623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Challenge Task 2: "Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques". Domain shifts are a critical problem for the application of ASD systems. Because domain shifts can change the acoustic characteristics of data, a model trained in a source domain performs poorly for a target domain. In DCASE 2021 Challenge Task 2, we organized an ASD task for handling domain shifts. In this task, it was assumed that the occurrences of domain shifts are known. However, in practice, the domain of each sample may not be given, and the domain shifts can occur implicitly. In 2022 Task 2, we focus on domain generalization techniques that detects anomalies regardless of the domain shifts. Specifically, the domain of each sample is not given in the test data and only one threshold is allowed for all domains. We will add challenge results and analysis of the submissions after the challenge submission deadline.

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