# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 3 Experiment and Analysis
### Comparison of Other Anomalous Sound Detection System
In contrast, both ASDNet and SpecMFN are more efficient while maintaining strong performance. Specifically, ASDNet has 85.4 M FLOPs and 0.51 M parameters, achieving reductions of 93.81% in FLOPs and 41.38% in parameters relative to LogMel-MFN [13], and 99.21% in FLOPs and 97.09% in parameters compared to Spectrogram-WaveNet [14]. SpecMFN has 184.2 M FLOPs and 1.05 M parameters, which represents a reduction of 86.65% in FLOPs compared to LogMel-MFN [13], and reductions of 98.29% in FLOPs and 93.88% in parameters compared to Spectrogram-WaveNet [14]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Fan** & **Pump** & **Slider** & **Valve** & **ToyCar** & **ToyConveyor** & **Average** \\ \cline{2-9}  & **AUC/pAUC** & **AUC/pAUC** & **AUC/pAUC** & **AUC/pAUC** & **AUC/pAUC** & **AUC/pAUC** & **AUC/pAUC** \\ \hline \multicolumn{9}{l}{**Multi-feature input methods**} \\ \multicolumn{9}{l}{STgram-MFN [13]} & 94.04/88.97 & 91.94/81.75 & 99.55/97.61 & 99.64/98.44 & 94.44/87.68 & 74.57/63.60 & 92.36/86.34 \\ SW-Wavenet [14] & 97.53/91.54 & 87.27/82.68 & 98.96/94.58 & 99.01/97.26 & 95.49/90.20 & 81.20/68.20 & 93.25/87.41 \\ ASD-AFPA [23] & 97.55/93.48 & 94.46/86.76 & **99.69/98.40** & 99.12/95.42 & 96.12/89.45 & 76.49/64.21 & 93.91/87.95 \\ CLP-SCF [39] & 96.98/93.23 & 94.97/87.39 & 99.57/97.73 & **99.89**/**99.51** & 95.85/**90.19** & 75.21/62.79 & 93.75/88.48 \\ LMS-MobileNetV3 [24] & 91.86/85.64 & 91.30/83.27 & 97.56/95.64 & 95.31/90.23 & 92.08/88.27 & **84.57**/**75.83** & 92.14/86.48 \\ \hline \multicolumn{9}{l}{**Single-feature input methods**} \\ \multicolumn{9}{l}{LogMel-MFN [13]} & 82.36/\(-\) & 87.74/\(-\) & 99.08/\(-\) & 89.91/\(-\) & 88.73/\(-\) & 78.17/\(-\) & 87.67/\(-\) \\ Spectrogram-WaveNet [14] & 83.45/\(-\) & 85.94/\(-\) & 98.00/\(-\) & 97.68/\(-\) & 94.54/\(-\) & 80.53/\(-\) & 90.03/\(-\) \\ LogMel-MobileNetV3 [24] & 82.28/71.31 & 84.91/73.28 & 93.62/87.58 & 88.32/79.32 & 87.81/80.36 & 84.42/75.21 & 86.89/77.84 \\ SpecMFN & **99.38**/**98.15** & **96.72**/**91.76** & 96.73/86.76 & 99.78/98.82 & **97.06**/90.14 & 76.51/66.00 & 94.36/**88.60** \\ ASDNet & 98.63/95.17 & 94.86/89.04 & 97.27/88.87 & 99.56/97.83 & 96.90/89.15 & 79.41/63.04 & **94.42**/87.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison between multi-feature and single-feature input methods. Boldface denotes the best results. The results demonstrate that ASDNet and SpecMFN achieve an optimal balance between performance and hardware efficiency, making them particularly well suited for embedded devices and mobile applications in resource-constrained environments. In contrast, while multi-feature methods offer superior performance for specific tasks, their significantly higher hardware costs restrict their applicability in low-resource scenarios. Methods with high FLOPs and parameter counts, such as SW-WaveNet, face substantial limitations in real-world applications. These observations highlight the effectiveness of our single-feature methods, which leverage spectral features and efficient model architectures to deliver substantial performance improvements while maintaining low computational costs. Figure 4 highlights the advantages of single-feature methods in terms of both performance and computational complexity. ASDNet and SpecMFN stand out due to their top-left positions (high AUC and low FLOPs) and small point sizes (low parameter counts), indicating their ability to achieve high performance while significantly reducing hardware costs, making them particularly suitable for resource-constrained environments. In contrast, other single-feature methods, such as LogMel-MFN [13] and Spectrogram-WaveNet [14], exhibit higher FLOPs and parameter counts, which lead to noticeably lower performance. This suggests that improvements in feature extraction or model architecture design could further enhance their performance, making them more efficient for practical applications. Meanwhile, multi-feature methods generally incur higher hardware costs.