## 3 Results  
The two trained models have different definitions for anomaly detection. The Outlier classifier is trained for distinguishing anomalies from normal sounds, so the outputs of the model are directly used as the anomaly scores. We also apply the same supervised settings shown in Figure 1c as the Outlier classifier to the network in Figure 2b, but it doesn't perform well. Different from the Outlier classifier, we train the ID Classifier to recognize different IDs of the same machine type and learn the hidden characteristics of the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Operator** & **Exp Size** & \(\#out\) & **SE** & **NL** & \(s\) \\ \hline conv3 \(\times\) 3 & - & 32 & - & HS & 2 \\ bneck3 \(\times\) 3 & 64 & 32 & - & RE & 1 \\ bneck3 \(\times\) 3 & 64 & 32 & - & RE & 2 \\ bneck3 \(\times\) 3 & 64 & 32 & - & RE & 1 \\ bneck3 \(\times\) 3 & 64 & 32 & \(\checkmark\) & RE & 2 \\ bneck3 \(\times\) 3 & 64 & 32 & \(\checkmark\) & RE & 1 \\ bneck3 \(\times\) 3 & 128 & 64 & - & HS & 2 \\ bneck3 \(\times\) 3 & 128 & 64 & - & HS & 1 \\ bneck3 \(\times\) 3 & 128 & 64 & - & HS & 1 \\ bneck3 \(\times\) 3 & 128 & 64 & - & HS & 1 \\ bneck3 \(\times\) 3 & 256 & 128 & \(\checkmark\) & HS & 1 \\ bneck3 \(\times\) 3 & 256 & 128 & \(\checkmark\) & HS & 1 \\ bneck3 \(\times\) 3 & 256 & 128 & \(\checkmark\) & HS & 1 \\ bneck3 \(\times\) 3 & 256 & 128 & \(\checkmark\) & HS & 2 \\ bneck3 \(\times\) 3 & 256 & 128 & \(\checkmark\) & HS & 1 \\ conv1 \(\times\) 1 & - & 512 & - & HS & 1 \\ GDConv32 \(\times\) 1 & - & 512 & - & - & 1 \\ conv1 \(\times\) 1 & - & 128 & - & - & 1 \\ \hline \hline \end{tabular} _#out_ refers to the number of out channels, SE refers to Squeeze-And-Excite block, HS refers to h-swish, RE refers to ReLU, \(s\) refers to stride, and NL refers to nonlinear.  
\end{table} 
Table 1: Architecture of MobileNet. normal sounds. We calculate the similarity between the embeddings of unseen sounds and corresponding normal sounds for anomaly detection in two ways: angle (Cosine similarity) and distance (OneClassSVM), and the final anomaly score is calculated as \"1-similarit\". It is worth noting that OneClassSVM is suitable for anomaly detection of the machine \"_ToyCar_\", Cosine similarity is suitable for other machine types according to our experiments. The comparison of our methods against other advanced approaches on the evaluation set of DCASE 2020 task 2. The evaluation indexes we use here are AUC (area under curve) and pAUC (partial area under curve), where AUC refers to the area under the ROC curve, and pAUC refers to the area under the ROC curve within the false positive rate [0, P]. Comparison results with existing models in DCASE are shown in Table 2, we can find our methods performs well on different machines, the Outlier classifier achieves the average AUC of 93.97% and average pAUC of 89.75% and the ID classifier achieves the average AUC of 92.09% and average pAUC of 87.81%. We summarize the advanced systems on DCASE 2020 task 2 into two categories: the classification-based models [13; 14; 15; 16; 17] and unsupervised-based models [9; 10; 11; 12], of these, the mode of Giri [13] based on multi-class classification ranked first in task 2 of DCASE2020. The unsupervised-based models like autoencoder, principal component analysis (PCA), KNN and normalizing flow only use normal sounds of the target machine as the training set. The classification-based models add another data sets to create a training set includeing multiple categories, and convert unsupervised anomaly detection to supervised or semi-supervised tasks. We can see that the classification-based models outperform the unsupervised-based models by a large margin, outlier samples can greatly help the model to recognize anomalous sounds. The experimental results confirm that the machine ID information is beneficial to accurately determine the classification boundary of the classifier and extract more distinguishing hidden features.