## 2 Related Work  
Urban noise classification is a key subfield of Environmental Sound Classification (ESC), aiming to identify noise sources in urban environments. Compared to general ESC tasks, this domain faces greater challenges, including highly overlapping sound sources, strong background interference, and dynamic variations over time and space. The main difficulties can be summarized as follows: (1) a lack of standardized noise categories--existing ISO and EC guidelines do not yet cover emerging sources, such as drones and high-speed trains, limiting their applicability to real-world scenarios; (2) high annotation costs--urban audio often contains overlapping sources, requiring annotators to refer to additional modalities, such as video or textual descriptions, increasing subjectivity and workload; and (3) data imbalance--certain sounds, such as those occurring during extreme weather or sudden accidents, are rare and difficult to collect, making it hard to build balanced datasets. To ensure the relevance and representativeness of the literature review, we included peer-reviewed studies published between 2014 and 2024 that employed publicly available datasets (e.g., ESC-50, UrbanSound8K) for ESC research, with a focus on those targeting urban noise scenarios or incorporating attention mechanisms. Studies unrelated to urban environments, or focusing solely on speech recognition, biomedical audio, or music classification, were excluded. This section first reviews the publicly available ESC datasets and their construction standards adopted in previous studies and then details the principles and methodological framework of our proposed dataset. This section first reviews publicly available environmental sound datasets and their construction standards used in previous studies. Subsequently, the principles and methodological framework for constructing the proposed dataset in this study are introduced in detail. In recent years, publicly available environmental sound datasets have played a crucial role in advancing urban noise classification and related research. ESC-50 and UrbanSound8K [7; 8] are among the most commonly used benchmark datasets for environmental sound classification. Both datasets source their audio samples from FreeSound, an open-access audio-sharing platform that allows users to upload and download environmental sound recordings. ESC-50 comprises 2000 audio clips spanning 50 sound categories, including human activities, natural sounds, and urban noise. Due to its precise manual annotations and extensive coverage, it has been widely used for evaluating traditional machine learning and deep learning models. In contrast, UrbanSound8K is specifically designed for urban noise classification and contains 8732 labeled audio clips covering 10 urban noise classes, such as air conditioners, car horns, children playing, and engine idling. This dataset provides real-world urban noise recordings, making it a valuable resource for assessing urban noise classification models. Apart from these, AudioSet [23] is currently the largest audio event classification dataset, developed by Google using audio clips extracted from YouTube. It contains over 2 million 10 s audio clips, covering 527 sound categories, including traffic noise, alarms, and industrial noise, among other urban noise sources. AudioSet employs weak supervision labeling, where annotations are assigned based on YouTube video metadata, descriptions, and automated labeling models, rather than precise manual labeling. Moreover, since the dataset relies on YouTube, some audio clips may become unavailable due to privacy settings or copyright restrictions, potentially affecting the reproducibility of experiments. Another category of datasets is those collected through acoustic sensors and in-field recordings, enhancing the realism and applicability of urban noise data. TAU Urban Acoustic Scenes 2020 Mobile [24], released by the DCASE community, contains 10 urban acoustic scene categories recorded across 12 European cities.