## 4 Conclusions  
In this paper, we introduce two classification-based models for the anomaly detection and conduct experiments in task 2 of DCASE 2020 challenge. Both models are trained with only normal sounds to learn the distribution characteristics of the normal sounds like most unsupervised methods, and then the unseen sounds are identified as the anomalous when they are outliers of normal sounds. The practical application of the model is mainly aimed at automatic machine fault detection, which is a key technology of the fourth industrial Revolution including factory automation based on artificial intelligence. The abnormal machine can be recognized by the sound of the machine running to speed up the process of industrial automation. In normal factories, it is extremely difficult to obtain the abnormal sound of machine operation. The model mentioned in this paper can identify abnormal samples in the data with a large number of normal samples and realize automatic detection of machine faults. We will continue to conduct in-depth research on this. Different from the unsupervised methods, we also use samples from other machine IDs to train the models in a supervised manner, so that the classification-based method can be used to find the decision boundary between the normal and outliers. The use of machine ID information helps to determine the decision boundary accurately and improve the ASD performance. At present, our work only achieves the best average accuracy of anomaly detection for six machine types, but does not achieve the best effect on all machine types. Further improving the generalization ability of the model will be our next improvement direction Table 2 demonstrates that the classification-based models outperform the unsupervised-based models significantly across all machine types, and our models outperform the state-of-the-art models, achieving an averaged AUC of 95.82% and an averaged pAUC of 92.32% with an ensemble strategy.  
Conceptualization, Y.W. and L.H.; methodology, Y.W., Y.Z. ([PERSON]) and Y.Z. ([PERSON]); software, Y.W., Y.Z. ([PERSON]) and Y.Z. ([PERSON]); validation, L.H., Y.W., Y.Z. ([PERSON]) and Y.Z. ([PERSON]).; formal analysis, Y.W., Y.Z. ([PERSON]) and Y.Z. ([PERSON]); investigation, Y.W., Y.Z. ([PERSON]) and Y.Z. ([PERSON]); resources, L.H., Y.X., Y.H. and S.X.; data curation, Y.W. and L.H.; writing--original draft preparation, Y.W. and L.H.; writing--review and editing, Y.Z. ([PERSON]), Y.Z. ([PERSON]) and L.H.; visualization, Y.W., Y.Z. ([PERSON]) and Y.Z. ([PERSON]); supervision, L.H.; project administration, L.H. and Y.W.; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. This data can be found here: [[[http://dcase.community/challenge2020/](http://dcase.community/challenge2020/)]([http://dcase.community/challenge2020/](http://dcase.community/challenge2020/)) (accessed on 1 October 2021)].  
Figure 4: Distribution of anomaly scores of the machine _“Valve”_. (**a,b**) are different machines of the same types. (**b**) shows that the model completely distinguishes normal and anomalous sounds, but (**a**) does not.