Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection

• URL: http://arxiv.org/abs/2204.01905v1
• Date: Tue, 5 Apr 2022 00:22:25 GMT
• Title: Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection
• Authors: Bingqing Chen, Luca Bondi, Samarjit Das
• Abstract summary: Anomaly detection has many important applications, such as monitoring industrial equipment. We propose a framework that adapts to new conditions with few-shot samples. We evaluate our proposed method on a recently-released dataset of audio measurements from different machine types.
• Score: 7.631596468553607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection has many important applications, such as monitoring industrial equipment. Despite recent advances in anomaly detection with deep-learning methods, it is unclear how existing solutions would perform under out-of-distribution scenarios, e.g., due to shifts in machine load or environmental noise. Grounded in the application of machine health monitoring, we propose a framework that adapts to new conditions with few-shot samples. Building upon prior work, we adopt a classification-based approach for anomaly detection and show its equivalence to mixture density estimation of the normal samples. We incorporate an episodic training procedure to match the few-shot setting during inference. We define multiple auxiliary classification tasks based on meta-information and leverage gradient-based meta-learning to improve generalization to different shifts. We evaluate our proposed method on a recently-released dataset of audio measurements from different machine types. It improved upon two baselines by around 10% and is on par with best-performing model reported on the dataset.

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