# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 5 Conclusions  
This paper addresses the challenge of balancing improved detection accuracy with reduced computational complexity in Anomalous Sound Detection. To tackle this, we propose an efficient self-supervised ASD method that integrates spectral features, lightweight network architectures, and clustering-based scoring mechanisms. Specifically, we replace traditional Log-Mel features with spectral features, which offer higher-resolution frequency details that significantly enhance the input features and improve detection accuracy. Ad  
Figure 5: Comparison of AUC and pAUC for different distance metrics used in the LOF algorithm. The y-axis represents the performance percentage, and the x-axis represents the different distance metrics. ditionally, we design two network architectures: (1) SpecMFN, a highly efficient feature extraction network combining SpecNet and MobileFaceNet, suitable for scenarios demanding high detection sensitivity and precision, and (2) ASDNet, a lightweight network optimized for resource-constrained environments. ASDNet achieves a significant reduction in computational complexity by reducing both the depth of convolutional layers and the number of parameters, making it ideal for deployment in low-resource settings. Furthermore, we systematically compare various anomaly scoring mechanisms, demonstrating the superiority of clustering-based methods in capturing the distribution characteristics of anomalous patterns. In contrast to traditional classification confidence-based methods, clustering-based scoring utilizes complex decision boundaries to more accurately identify a broader range of anomalous patterns. Experimental results underscore the significant advantages of ASDNet and SpecMFN in terms of both performance and computational complexity. ASDNet achieves average AUC and pAUC values of 94.42% and 87.18%, respectively, while SpecMFN reaches 94.36% and 88.60%. Compared to LogMel-MFN, ASDNet improves AUC and pAUC by 6.75% and 9.34%, respectively, while SpecMFN achieves corresponding improvements of 6.69% and 10.09%. In terms of computational complexity, ASDNet requires only 85.4 M FLOPs and 0.51 M parameters, whereas SpecMFN requires 184.2 M FLOPs and 1.05 M parameters. Compared to LogMel-MFN, which demands 1.38 G FLOPs and 0.87 M parameters, ASDNet reduces FLOPs by 93.81% and parameter count by 41.38%, while SpecMFN achieves an 86.65% reduction in FLOPs. These results demonstrate that the proposed methods not only deliver outstanding performance but also significantly reduce computational complexity, making them especially suitable for resource-constrained embedded devices and edge computing environments. In practical applications, the choice of network architecture and anomaly scoring methods should be tailored to the specific requirements of the task and the operational environment. SpecMFN, with its higher pAUC values, is particularly well suited for applications that require high sensitivity and precision. For instance, in high-precision industrial equipment monitoring (such as the operation of precision machinery), SpecMFN effectively minimizes the potential damage caused by false positives, thereby enhancing fault detection accuracy and preventing errors or downtime due to false alarms. In contrast, ASDNet is a lightweight network optimized for resource-constrained environments, making it ideal for low-cost consumer electronics, such as smart home devices or wearables. ASDNet reduces computational load and improves real-time responsiveness while maintaining accurate detection of anomalous sounds. Different anomaly scoring methods are more suitable for different scenarios. The K-Means clustering method performs well in general applications, with an average AUC of 94.42% and pAUC of 87.18%, making it reliable for routine industrial equipment fault detection. However, for more specialized applications, selecting the most appropriate anomaly scoring method is crucial.