# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
However, despite their effectiveness, Log-Mel features have notable limitations, particularly in their inability to capture sufficient frequency details, which can hinder their effectiveness in scenarios requiring fine-grained spectral resolution. Attention mechanisms have played a pivotal role in advancing ASD systems by enhancing feature representations and improving detection performance. [PERSON] et al. [23] utilized self-attention mechanisms to refine Log-Mel features, resulting in significant improvements in temporal-frequency representations and achieving superior detection accuracy. Similarly, [PERSON] et al. [15] introduced temporal self-attention mechanisms, en abling more precise temporal-frequency representations and further strengthening the model's ability to identify complex anomalous patterns. To extend these advancements, [PERSON] et al. [28] developed a Multi-Dimensional Attention Module (MDAM), which applies attention independently across three dimensions: time, frequency, and channel. By selectively emphasizing frequency bands with discriminative information and semantically relevant time frames, MDAM effectively enhanced the network's feature representation capabilities and improved its robustness in ASD applications. For network architecture, [PERSON] et al. [25] optimized the MobileFaceNet [29] structure for ASD tasks, achieving a balance between simplified network complexity and improved detection performance. [PERSON] [30] employed a modified ResNet [31] architecture, which significantly improved classification accuracy and overall detection performance. [PERSON] et al. [14] integrated WaveNet [32] as a classification network, demonstrating its superior capability in handling complex sound patterns in ASD tasks. Moreover, [PERSON] et al. [24] utilized MobileNetV3 [33] for classification, achieving a balance between performance and computational efficiency, making it particularly suitable for resource-constrained environments. Collectively, these studies highlight that optimizing network architectures and incorporating advanced attention mechanisms can effectively improve anomalous sound detection systems, addressing the challenges of complex and resource-limited application scenarios. While these methods have significantly improved detection performance, they often do so at the cost of increased computational overhead. For instance, the integration of additional feature processing and complex attention mechanisms enhances detection capabilities but substantially increases the model's parameter count and inference time [16]. Similarly, adopting more complex network architectures can lead to higher detection accuracy but also significantly escalates computational complexity and resource requirements [14; 30], thereby constraining their deployment on resource-limited edge devices. This underscores a critical challenge in current ASD research: achieving an optimal balance between detection performance and model complexity. Addressing this challenge necessitates the development of approaches that leverage lightweight neural networks and fewer input features while maintaining high detection accuracy. Such strategies not only reduce computational overhead but also align with the resource efficiency constraints of edge devices, facilitating the wider adoption of ASD technology in real-world applications. Furthermore, regarding the computation of anomaly scores, current ASD methods primarily rely on classification confidence [14; 15; 28]. Although this approach improves the detection performance of classifiers through pseudo-labeling, its relatively simplistic decision boundaries constrain its capability to capture complex anomalous patterns effectively. In contrast, clustering-based methods, such as K-Means [34], Gaussian Mixture Models (GMMs) [35], and Local Outlier Factor (LOF) [36], as well as feature-space-based scoring methods like cosine similarity [37], demonstrate superior representational capabilities.