# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection  
[PERSON]  
1 Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; [EMAIL_ADDRESS]. complex, non-repetitive patterns, making it difficult to construct comprehensive datasets or accurately label all potential anomalous scenarios [7]. These limitations highlight the inadequacy of traditional supervised ASD methods, which rely heavily on labeled data and are therefore unsuitable for many practical industrial applications [7]. In response to these challenges, unsupervised ASD methods have emerged as a more practical and efficient alternative. Unlike supervised approaches, these methods require only normal data for model training, which are more readily available in industrial environments [8]. In an unsupervised ASD framework, the model is trained using exclusively normal sounds. During the detection phase, the trained model calculates an anomaly score for incoming sounds. If the score exceeds a predefined threshold, the sound is classified as anomalous; otherwise, it is considered normal. Self-Supervised Learning (SSL) has recently emerged as a critical extension of unsupervised ASD, leveraging pseudo-labels generated from audio metadata (e.g., device IDs) to improve detection performance [7]. SSL methods train classification models to learn the distribution of normal sound characteristics. In contrast to traditional AutoEncoder (AE)-based approaches, which calculate anomaly scores using reconstruction errors [8; 9; 10; 11; 12], SSL provides greater stability and robustness when dealing with diverse and complex anomalous patterns [13; 14; 15; 16; 17; 18; 19; 20; 21]. Moreover, SSL addresses the limitation of AE methods that require separate models for each device by enabling cross-device knowledge generalization through the use of pseudo-labels [13]. This approach reduces the complexity of training and deployment processes while improving the detection of previously unseen anomalies, making SSL a reliable and efficient solution for ASD in resource-constrained environments. Current research on ASD systems primarily focuses on improving detection performance, often achieved by increasing input features, incorporating attention mechanisms, or optimizing network architectures [13; 14; 22; 23; 24; 25]. Regarding input features, [PERSON] et al. [13] proposed TgramNet, a framework that extracts temporal information and combines it with Log-Mel features. By feeding these enriched features into a classification network, their method achieved substantial improvements in detection accuracy. Similarly, [PERSON] [22] introduced a specialized network to process spectral features alongside Log-Mel features. By fusing Log-Mel and spectral features at the latent feature stage, this approach significantly improved the model's robustness and generalization, underscoring the importance of spectral features in detecting anomalous patterns. Extending this concept, [PERSON] et al. [24] enhanced input diversity by combining Log-Mel features with spectral features extracted via SincNet [26], further enriching input representations and improving detection performance. [PERSON] et al. [16] advanced feature fusion techniques by integrating multi-spectral and multi-temporal features, enabling the capture of complex anomalous patterns with higher accuracy. Collectively, these studies highlight the critical role of diversifying input features in enriching data representation, improving model expressiveness, and ultimately achieving more accurate detection of anomalous sounds. From the perspective of single-feature performance, Log-Mel features are widely regarded as the most effective, outperforming other feature extraction methods such as MFCCs [27] and network-extracted features like those generated by SincNet [24].