# An Enhanced Contrastive Ensemble Learning Method for Anomaly Sound Detection
## 4 Discussion  
Based on the experimental results, the proposed model exhibits superior performance compared to traditional classification models and reconstruction methods. Within the MIMI dataset, compared with other existing methods, the test AUC results of the proposed method remained stable among the four machine categories, with the maximum difference in AUC within 5.1% while the other methods could reach from 9.2% to 15.4%. Based on the data of the fan, the proposed method is able to achieve the highest increase in AUC value. In the DCASE2020 task2 dataset, the model also exhibits excellent anomaly detection performance. In the MIMI DUE dataset, the proposed model can effectively perform anomaly detection under domain shift without using any domain adaptation operations and outperform several baseline models.  
Meanwhile, based on the ablation results, it can be observed that the model utilizing the fusion method achieves better performance in terms of detection accuracy for certain categories. This is attributed to the model allocating greater weight to feature channels with more prominent feature expressions, thereby effectively enhancing the model's overall performance.  
Based on the results of the above multiple experiments, it can be demonstrated that the proposed model exhibits excellent anomaly sound detection performance under multiple public datasets. The proposed time-frequency domain fusion model and statistical feature model can effectively conduct abnormal detection without being trained with real abnormal samples in reality, and avoid the model bias problem caused by data imbalance. Moreover, by learning the pseudo-anomaly samples generated by the abnormal sample generator, the model can also spontaneously explore the feature distribution of potential real anomaly samples. At the same time, considering the resource constraints that may exist in real manufacturing environments, the proposed model adopts multiple lightweight design approaches that enable it to resolve potential resource conflicts.  
Despite the excellent ASD performance of the proposed model, there are still some issues that need to be addressed. For example, the proposed model employs a statistical-based approach to counteract the effect of data imbalance, but based on the result of the ablation experiment, it can be found that the approach is not effective in providing accurate feature induction information to help the model significantly improve its performance when facing some of the non-stationary signals. Meanwhile, since the input samples may contain noise information, the use of the statistical approach may surround too much disturbing information and affect the model performance instead. Furthermore, taking into account the influences of temperature and humidity on sound and production machines, when the manufacturing system is subjected to drastic weather variations, the informative features of the sound are changed, thereby leading to a decline in the model's performance.  
Even though the statistical model is employed without fully accounting for the noise in the data, the issue of the feature space distribution becoming overly concentrated is seen as the data expand.However, the proposed method employs an ensemble discriminant-based approach in the inference stage, and the weight assigned to the statistical model is considerably lower than that of the neural network model. Therefore, although noise can affect the modeling capability of the statistical model, it does not affect the overall detection performance of the ensemble model.