# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 3 Experiment and Analysis
### Comparison of Different Method of Anomaly Score  
Table 4 presents a comparative analysis of the performance of various anomaly scoring methods. The PROB method calculates anomaly scores based on the Softmax probability output, whereas other methods--namely COS, GMM, KMEANS, and LOF--utilize latent features extracted from a neural network to model data distributions. Since the PROB method relies on the Softmax output of the classifier, it reflects the confidence of a sample belonging to a pseudo-label category. Notably, when machine IDs are used as pseudo-labels, the classifier primarily focuses on distinguishing between different machines, rather than differentiating between normal and anomalous points. As a result, PROB performs suboptimally in certain anomaly distributions. For example, on the ToyConveyor type, PROB achieves an AUC of 69.05% and a pAUC of 56.60%, both of which are significantly lower than those of other methods, highlighting its limitations in handling specific machine types. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Machine Type**} & \multicolumn{2}{c}{**LogMel-MFN**} & \multicolumn{2}{c}{**SpecMFN**} & \multicolumn{2}{c}{**ASDNet**} \\ \cline{2-7}  & **AUC** & **pAUC** & **AUC** & **pAUC** & **AUC** & **pAUC** \\ \hline Fan & 92.30 & 84.06 & **99.39** & **98.15** & 98.63 & 95.17 \\ Pump & 92.20 & 84.30 & **96.72** & **91.76** & 94.86 & 89.04 \\ Valve & **99.02** & **95.01** & 96.73 & 86.76 & 97.26 & 88.87 \\ Slider & 99.17 & 96.37 & **99.78** & **98.82** & 99.56 & 97.83 \\ ToyCar & 94.41 & 84.92 & **97.06** & **90.14** & 96.80 & 89.15 \\ ToyConveyor & 71.66 & 61.92 & 76.51 & **66.00** & **79.41** & 63.04 \\ Total & 91.46 & 84.43 & 94.36 & **88.60** & **94.42** & 87.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different input features and classification networks in terms of average AUC(%) and pAUC(%). Boldface denotes the best results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **Flops** & **\# Params** \\ \hline LogMel-MFN & 139.9 M & 0.87 M \\ SpecMFN & 184.2 M & 1.05 M \\ ASDNet & 85.4 M & 0.51 M \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Complexity comparison of different input and classification networks in terms of FLOPs and the number of parameters. In contrast, the methods COS, GMM, KMEANS, and LOF model data distributions directly from the latent features extracted by the neural network, allowing them to capture anomaly patterns more effectively. These methods avoid reliance on pseudo-labels, offering greater flexibility and robustness in detecting anomalies. Specifically, KMEANS outperforms other methods on the Fan and Valve types, with AUC and pAUC scores of 98.63% and 95.17%, and 97.26% and 88.87%, respectively. This demonstrates KMEANS' ability to accurately identify anomalies and its strong adaptability across different machine types. The GMM method excels on the Slider type, achieving an AUC of 99.79% and a pAUC of 98.94%. On the ToyCar type, GMM achieves the highest AUC of 97.18%, with a pAUC near optimal at 89.76%, further emphasizing its strength in these two types. The LOF method performs outstandingly on both the ToyConveyor and Pump types, achieving an AUC of 96.35% for the Pump type. For the ToyConveyor type, the AUC is 81.06% and the pAUC is 66.20%. The COS method demonstrates relatively balanced performance across a wide range of machine types, with results close to the best performance, especially on the Fan and Pump types. In summary, KMEANS ranks highest among all methods, with an AUC of 94.42% and a pAUC of 87.18%, owing to its consistently excellent performance and adaptability across a variety of machine types. GMM ranks second, as it excels in detecting anomalies in the Slider and ToyCar types, although its overall performance is slightly lower than that of KMEANS. The COS method ranks third, offering balanced performance across all machine types.