An Enhanced Contrastive Ensemble Learning Method for Anomaly Sound Detection

This paper proposes an enhanced contrastive ensemble learning method for anomaly sound detection. The proposed method achieves approximately 6% in the AUC metric in some categories and achieves state-of-the-art performance among self-supervised models on multiple benchmark datasets. The proposed method is effective in automatically monitoring the operating conditions of the production equipment by detecting the sounds emitted by the machine, to provide an early warning of potential production accidents. This method can significantly reduce industrial monitoring costs and increase monitoring efficiency to improve manufacturing facility productivity effectively. Existing detection methods face challenges with data imbalance caused by the scarcity of anomalous samples, leading to performance degradation. This paper proposes an enhanced data augmentation method that improves model robustness by allowing the data to retain the original features while adding noise close to the real environment through a simple operation. Secondly, model feature extraction is enhanced by using channel attention to fuse time-frequency features. Thirdly, this paper proposes a simple anomaly sample generation method, which can automatically generate real pseudo anomaly samples to help the model gain anomaly detection capability and reduce the impact of data imbalance. Finally, this paper proposes a statistical-based bias compensation that further mitigates the impact of data imbalance by distributing samples through statistical induction. Experimental verification confirms that these changes enhance anomalous sound detection capability.