# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 3 Experiment and Analysis
### Comparison of Other Anomalous Sound Detection System  
This section provides a comprehensive comparative analysis of single-feature and multi-feature methods in terms of both performance and computational complexity for ASD systems. A balanced K-Means method is used to calculate anomaly scores. Table 5 presents a detailed comparison of AUC and pAUC performance across various tasks, highlighting the significant improvements achieved by our proposed single-feature methods (ASDNet and SpecMFN), which are based on spectral features. Table 6 further analyzes the computational complexity of each method, emphasizing the superior hardware efficiency of the single-feature approaches. Additionally, the overall advantages of ASDNet and SpecMFN are visually illustrated in Figure 4: they are positioned at the top (indicating \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Machine Type**} & \multicolumn{2}{c}{**PROB**} & \multicolumn{2}{c}{**COS**} & \multicolumn{2}{c}{**GMM**} & \multicolumn{2}{c}{**KMEANS**} & \multicolumn{2}{c}{**LOF**} \\ \cline{2-11}  & **AUC** & **pAUC** & **AUC** & **pAUC** & **AUC** & **pAUC** & **AUC** & **pAUC** & **AUC** & **pAUC** \\ \hline Fan & 97.98 & 94.60 & 98.59 & 95.32 & 98.33 & 94.13 & **98.63** & **95.17** & 98.38 & 94.52 \\ Pump & 95.12 & **90.25** & 95.26 & 89.91 & 94.70 & 87.41 & 94.86 & 89.04 & **96.35** & 90.10 \\ Valve & 83.41 & 71.11 & 93.80 & 84.81 & 97.16 & 87.56 & **97.26** & **88.87** & 88.59 & 60.99 \\ Slider & 99.36 & 97.62 & 99.57 & 98.05 & **99.79** & **98.94** & 99.56 & 97.83 & 99.77 & 98.83 \\ ToyCar & 86.87 & 81.29 & 94.56 & 85.89 & **97.18** & 89.76 & 96.80 & 89.15 & 96.33 & **89.97** \\ ToyConveyor & 69.05 & 56.60 & 78.87 & 63.21 & 78.31 & 60.64 & 79.41 & 63.04 & **81.06** & **66.20** \\ Total & 88.63 & 81.91 & 93.44 & 86.20 & 94.25 & 86.41 & **94.42** & **87.18** & 93.41 & 83.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of different methods of anomaly scoring in terms of average AUC(%) and pAUC(%). Boldface denotes the best results. high AUC), to the left (indicating low FLOPs), and with smaller point sizes (indicating low parameter counts). These positions reflect an optimal balance between performance and hardware cost. Table 5 summarizes the performance comparison between our methods and previous approaches, categorized into single-feature and multi-feature methods. The results indicate that the proposed single-feature methods (ASDNet and SpecMFN) outperform other single-feature methods across most tasks and evaluation metrics. For specific machine types, ASDNet and SpecMFN significantly outperform other single-feature methods in tasks such as Fan, Pump, Slider, Valve, and ToyCar. Notably, they even surpass multi-feature methods in tasks like Fan, Pump, and ToyCar. On Slider and Valve tasks, their performance is comparable to that of multi-feature methods, further validating their robustness and applicability across different tasks. In terms of overall average metrics, ASDNet achieves an average AUC of 94.42% and pAUC of 87.18%, showing improvements of 6.75% and 4.39%, respectively, over LogMel-MFN [13] and Spectrogram-WaveNet [14] in AUC, and 9.34% and 7.15% in pAUC. SpecMFN achieves an average AUC of 94.36% and pAUC of 88.60%, with improvements of 6.69% and 4.33% in AUC, and 10.09% and 7.61% in pAUC, respectively. Notably, ASDNet achieves the highest AUC among all single-feature methods, while SpecMFN's performance is comparable to the best multi-feature methods. For pAUC, SpecMFN outperforms all other methods. Both ASDNet and SpecMFN exhibit exceptional stability and generalization across multiple tasks. Table 6 presents a comparison of the computational complexity of different methods. In general, multi-feature methods tend to have higher FLOPs and parameter counts compared to single-feature methods. For instance, SW-WaveNet [14] requires 21.56 G FLOPs and has 34.47 M parameters, making it the most computationally and memory-intensive model.