# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 4 Discussion  
### Configuration Selection of ASDNet Architecture  
In this section, we evaluate the performance of ASDNet under different configurations of layers, channels, and kernel sizes. The choice of an eight-layer network, 128 channels, and specific kernel sizes was based on the balance of performance observed in the experiments. As shown in Table 7, this configuration effectively balances computational complexity with high detection accuracy. Through comparison of various configurations, we identified this setup as the most optimal and selected it as the final implementation of ASDNet. In evaluating different network depths, we test configurations with 7, 8, 9, and 10 layers. The experimental results show that the eight-layer network (ASDNet) achieves the best performance, with an AUC of 94.42% and a pAUC of 87.18%. The nine-layer configuration shows a slight improvement in pAUC (87.21%) but a minor decrease in AUC (94.10%). The 10-layer configuration exhibits further performance decmidrule with an AUC of 93.83% and a pAUC of 86.98%. The seven-layer configuration has the lowest performance, with an AUC of 91.99% and a pAUC of 84.27%, indicating that fewer layers may limit model expressiveness. Overall, while adding layers may improve performance in some cases, it does not guarantee substantial improvements and may increase computational complexity. Thus, the eight-layer configuration strikes an optimal balance between accuracy and computational efficiency, making it the final choice. Figure 4: Comparison of the model size and complexity (number of parameters and Flops), where the model size is illustrated by the size of the circles, and the number of Flops is presented in logarithmic scale. For channel number selection, we test four configurations: fixed channels (64, 128, 192, 256) and three configurations with varying channels (Option A, Option B, Option C). The results show that as the number of channels increased, the model's performance significantly improved, particularly when increasing from 64 to 128 channels, resulting in notable improvements in both AUC and pAUC. However, further increases in channel numbers led to performance degradation. For example, with 192 channels, the AUC dropped to 94.07%, and with 256 channels, it further decreases to 93.84%, while the pAUC drops to 86.57%. Meanwhile, computational complexity increases significantly. Among the varying channel configurations, Option A (increasing-then-decreasing channels) provides a good balance between performance and complexity. Although Option C achieves a higher pAUC of 87.77%, its computational complexity increases significantly, with FLops at 307.77 M and parameters at 1.23 M, while Option B does not show a performance improvement. Based on these results, we ultimately select the configuration with 128 channels, as it offers high detection accuracy while effectively controlling computational complexity. For kernel size selection, the results in Table 7 show that as the kernel size increases, both AUC and pAUC improve, but at the cost of increased computational cost and parameter count. Comparing different kernel configurations--including the decreasing configuration used in this study, Option D (increasing kernel size), and Option E (increasing-then-decreasing)--we find that the configuration selected for this study achieves the best performance in terms of AUC and pAUC, while also minimizing computational cost and parameter count. Therefore, we ultimately select this configuration.