## 1 Introduction  
With the accelerating pace of global urbanization and the continuous rise in population density, noise pollution has emerged as a major challenge in contemporary urban environmental management [1, 2]. Modern urban soundscapes exhibit substantial complexity, involving not only conventional noise sources--such as transportation systems, construction sites, industrial facilities, and public events--but also emerging sources of urban acoustic disturbance, including electric vehicles, drone swarms, shared mobility systems, and smart infrastructure technologies [3, 4]. Empirical research has demonstrated that prolonged exposure to sound pressure levels exceeding 65 dB can induce a range of health risks, including circadian rhythm disturbances, elevated levels of stress hormones, irreversible hearing damage, and a notably increased risk of cardiovascular diseases [5]. Given the dynamic and evolving nature of urban acoustic environments, the development of accurate and efficient automated noise source recognition systems has become a critical technical bottleneck in enabling scientific governance and promoting sustainable urban acoustic management. Environmental Sound Classification (ESC), which focuses on the automatic identification of non-speech, non-music environmental sounds, has emerged as a core task in acoustic analysis for smart cities, ecological monitoring, and public safety. Since 2013, the field has seen major developments in standardized datasets, beginning with the DCASE Challenge and its TUT Sound Events dataset [6], which established a reproducible framework for real-world sound classification. General-purpose datasets such as ESC-50 [7] and Urban-Sound8K [8], both released in 2015, have since become widely used benchmarks. While they enabled rapid model development, these datasets mainly consist of short, isolated sound clips and lack fine-grained, context-aware annotations. To address these limitations, a number of domain-specific datasets were introduced. For example, InsectSound1000 [9] supported insect sound classification in ecological contexts, while FSC22 [10] targeted forest soundscape monitoring. The MAVD dataset [11] addressed annotation challenges in high-noise, overlapping-source environments. In the urban domain, RoadSound14k [12]--developed by [PERSON] et al.--provided annotated road noise under various traffic conditions, whereas SONYC-UST-V2 [13], constructed from over 18,000 acoustic sensor recordings in New York City, enabled large-scale urban sound monitoring. However, these datasets commonly lack continuous and context-rich urban noise categories and suffer from limited geographic and environmental diversity, making them difficult to generalize across real-world urban settings. In addition to the growing availability of datasets, research in ESC is experiencing a shift from broad acoustic scene recognition toward fine-grained classification of specific sound events. Early systems largely relied on handcrafted features combined with shallow classifiers such as K-Nearest Neighbors (KNN), Gaussian Mixture Models (GMM), and Support Vector Machines (SVM) [9; 14], which performed reasonably well on constrained tasks. A significant breakthrough came when [PERSON] [15] first introduced Convolutional Neural Networks (CNNs) into ESC. Subsequently, SoundNet [16] addressed data scarcity through cross-modal learning, while deeper architectures such as Residual Network (ResNet) [17] and CNN-Long Short-Term Memory (CNN-LSTM) hybrids [18] further advanced sequence modeling and spectral discrimination. In recent years, attention mechanisms have been incorporated into CNN-Recurrent Neural Network (CNN-RNN) frameworks [19; 20], improving the ability to selectively focus on salient acoustic features under complex conditions. Collectively, these studies illustrate how deep learning methods have significantly advanced ESC capabilities in fine-grained sound classification and context-aware modeling, laying a robust foundation for real-world applications.