## 4 Experiments and Results
### Results
ANP (yellow bubble, 3.79 M parameters) and PAE (light blue bubble, 7.4 M parameters) show some improvements in performance, but their parameters increase significantly, leading to much higher model complexity, particularly for PAE, with a parameter count as high as 7.4 M, which far exceeds our method; moreover, the performance gain is not substantial, indicating low parameter efficiency. In contrast, our method achieves the best balance between performance and efficiency. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **AUC** & **pAUC** \\ \hline AE [35] & 65.26\% & 60.88\% \\ EGBAD [38] & 70.38\% & 63.19\% \\ IDNN [39] & 69.99\% & 62.26\% \\ ANP [40] & 71.50\% & 63.35\% \\ PAE [41] & 72.17\% & 63.27\% \\ Proposed & **80.67**\% & **70.33**\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation results on our collected wind blade test data, where the bold font indicates the best performance. Figure 10: Evaluation results on ToyADMOS test data. In addition, we also studied the interpretability of the model for a single sample. It is worth mentioning that the anomaly score is calculated based on Equations (13)-(15) and (20). As shown in Figure 12, the biggest difference between the audio of a faulty wind turbine blade and the audio of normal wind turbine equipment is the frequency at 2000-4000 Hz. The energy of the former in this part is significantly stronger than that of the latter, as shown in the white box in the upper half of Figure 12 and the red box in the lower half. This part of the energy indicates possible abnormal noise or damage of the equipment when it is in working condition. During the inference phase, we found that our model can capture such features. Therefore, the anomaly score of the former is 0.73 after inference, which is significantly higher than the latter's 0.36. This shows that the model has learned the domain knowledge in this area through a large amount of data combined with the phase characteristics of the latent space and can make accurate judgments on real data. In order to study the impact of different hyperparameter settings and model structures on performance, we conducted ablation experiment on the collected wind turbine blade audio data. Similarly, it was mainly measured by two metrics, AUC and pAUC. As shown in Table 5, the experimental results show that the choice of model structure and hyperparameters has a significant impact on performance. First, when the PA module is removed (w/o PA), the AUC is 76.26% and the pAUC is 67.79%. This is due to the lack of balance of phase information in the latent space caused by the absence of the PA module. In addition, when the discriminator is removed (w/o \(\mathcal{D}\)) and only general loss is used as the training target, the AUC is only 61.27% and the pAUC is 55.51%, which shows that adversarial training is crucial to improving the performance of unsupervised tasks. On the other hand, we analyze the impact of hyperparameter combinations. We tested six different weight combinations of loss functions in the experiment, and the optimal combination \(w_{1}=1,w_{2}=50,w_{3}=1\) significantly outperformed other settings in both AUC and pAUC. Compared with the second-best combination \(w_{1}=50,w_{2}=1,w_{3}=1\), the AUC is improved by 3.29% and the pAUC is improved by 2.44%. In addition, compared with the setting of removing the PA module (w/o PA), the AUC of this combination is improved by 4.41% and the pAUC is improved by 2.54%; compared with the setting of removing the discriminator \(\mathcal{D}\) (w/o \(\mathcal{D}\)), the AUC is improved by 19.40% and the pAUC is improved by 14.82%. These comparisons can verify the positive effect of the settings we adopted in the overall architecture. Figure 11: Scatter plot based on accuracy and number of parameters.